Phylogenetic Analysis
Marian L. Schmidt
May 26th, 2017
library(ggplot2)
library(devtools)
library(phyloseq)
library(picante)
library(tidyr)
library(forcats)
library(dplyr)
library(tibble)
library(cowplot)
library(picante) # Will also include ape and vegan
library(car) # For residual analysis
library(sandwich) # for vcovHC function in post-hoc test
library(MASS) # For studres in plot_residuals function
library(caret) # For cross validation
library(pander) # For pretty tables
library(glmnet) # For lasso and ridge regressions
source("code/Muskegon_functions.R")
source("code/set_colors.R") Map of Muskegon Lake
library(ggmap)
mussk <- get_map(c(-86.35, 43.21, -86.235, 43.265), #left/bottom/right/top
zoom = 13, maptype = "satellite", source= "google")
ggmap(mussk) + xlab("Longitude") + ylab("Latitude") +
theme(axis.text = element_text(size = 10, color = "black"),
axis.title = element_text(size = 12, color = "black", face = "bold"))
Load data
# Loads a phyloseq object named otu_merged_musk_pruned)
load("data/surface_PAFL_otu_pruned_raw.RData")
# The name of the phyloseq object is:
surface_PAFL_otu_pruned_raw ## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 7806 taxa and 24 samples ]
## sample_data() Sample Data: [ 24 samples by 44 sample variables ]
## tax_table() Taxonomy Table: [ 7806 taxa by 8 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 7806 tips and 7804 internal nodes ]# Remove doubletons!
surface_PAFL_otu_pruned_rm2 <- prune_taxa(taxa_sums(surface_PAFL_otu_pruned_raw) > 2, surface_PAFL_otu_pruned_raw)
surface_PAFL_otu_pruned_rm2## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 2979 taxa and 24 samples ]
## sample_data() Sample Data: [ 24 samples by 44 sample variables ]
## tax_table() Taxonomy Table: [ 2979 taxa by 8 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 2979 tips and 2977 internal nodes ]# Remove tree for computational efficiency
surface_PAFL_otu_pruned_notree_rm2 <- phyloseq(tax_table(surface_PAFL_otu_pruned_rm2), otu_table(surface_PAFL_otu_pruned_rm2), sample_data(surface_PAFL_otu_pruned_rm2))
# Gather the metadata in a dataframe to play with
metadata <- data.frame(sample_data(surface_PAFL_otu_pruned_notree_rm2)) %>%
mutate(fraction = factor(fraction, levels = c("WholePart","WholeFree")),
lakesite = factor(lakesite, levels = c("MOT", "MDP", "MBR", "MIN")),
fraction = fct_recode(fraction, Particle = "WholePart", Free = "WholeFree"),
lakesite = fct_recode(lakesite, Outlet = "MOT", Deep = "MDP", Bear = "MBR", River = "MIN"))
row.names(metadata) <- metadata$norep_filter_name
# Replace the sample data
sample_data(surface_PAFL_otu_pruned_notree_rm2) <- metadataProduction over Station and Season
ggplot(dplyr::filter(metadata, year == "2015" & fraction %in% c("Free", "Particle") &
norep_filter_name != "MOTEJ515"),
aes(x = lakesite, y = log10(as.numeric(fraction_bac_abund)), fill = fraction)) +
geom_bar(stat = "identity", color = "black", position=position_dodge()) +
facet_grid(. ~ season) +
scale_y_continuous(expand = c(0,0)) +
scale_fill_manual(values = fraction_colors) +
guides(fill=guide_legend(nrow=1,byrow=TRUE)) +
xlab("Lake Station") + ylab("Log10(Bacterial Cells/mL)") +
theme(axis.text.x = element_text(angle = 30, vjust = 1, hjust = 1),
legend.position = "bottom", legend.title = element_blank(),
plot.margin = unit(c(0.25, 0.25, 0.25, 0.5), "cm")) #top, right, bottom, and left margins)
prod1 <- ggplot(dplyr::filter(metadata, year == "2015" & fraction %in% c("Free", "Particle")),
aes(x = lakesite, y = frac_bacprod, fill = fraction)) +
geom_bar(stat = "identity", color = "black", position=position_dodge()) +
geom_errorbar(aes(ymin = frac_bacprod - SD_frac_bacprod, ymax = frac_bacprod + SD_frac_bacprod),
position = position_dodge(width = 0.75), width = 0.25) +
facet_grid(. ~ season) +
scale_y_continuous(expand = c(0,0), limits = c(0, 80),
breaks = seq(from = 0, to = 80, by = 15)) +
scale_fill_manual(values = fraction_colors) +
guides(fill=guide_legend(nrow=1,byrow=TRUE)) +
xlab("Lake Station") + ylab("Community Production\n(ug C/L/day)") +
theme(axis.text.x = element_blank(), #element_text(angle = 30, vjust = 1, hjust = 1),
axis.title.x = element_blank(),
legend.position = c(0.65, 0.9), legend.title = element_blank(),
plot.margin = unit(c(0.25, 0.25, 0.25, 0.25), "cm")) #top, right, bottom, and left margins)
prod2 <- ggplot(dplyr::filter(metadata, year == "2015" & fraction %in% c("Free", "Particle")),
aes(x = lakesite, y = fracprod_per_cell_noinf, fill = fraction)) +
geom_bar(stat = "identity", color = "black", position=position_dodge()) +
facet_grid(. ~ season) +
scale_y_continuous(expand = c(0,0)) +
scale_fill_manual(values = fraction_colors) +
guides(fill=guide_legend(nrow=1,byrow=TRUE)) +
xlab("Lake Station") + ylab("Per-Capita Production \n(ug C/cell/day)") +
theme(axis.text.x = element_text(angle = 30, vjust = 1, hjust = 1),
legend.position = c(0.65, 0.9), legend.title = element_blank(),
plot.margin = unit(c(0.25, 0.25, 0.25, 0.5), "cm")) #top, right, bottom, and left margins)
########## LONG FORMAT OF TOTAL PRODUCTIVITY
ggplot(dplyr::filter(metadata, year == "2015" & fraction == "Free"),
aes(x = lakesite, y = tot_bacprod)) +
geom_bar(stat = "identity", color = "black", fill = "grey", position=position_dodge()) +
geom_errorbar(aes(ymin = tot_bacprod - SD_tot_bacprod, ymax = tot_bacprod + SD_tot_bacprod),width = 0.25) +
facet_grid(season ~., scales = "free") +
scale_y_continuous(expand = c(0,0), limits = c(0, 80),
breaks = seq(from = 0, to = 80, by = 15)) +
scale_fill_manual(values = fraction_colors) +
guides(fill=guide_legend(nrow=1,byrow=TRUE)) +
xlab("Lake Station") + ylab("Community Production\n(ug C/L/day)") +
theme(legend.position = c(0.85, 0.9), legend.title = element_blank(),
plot.margin = unit(c(0.25, 0.25, 0.25, 0.25), "cm")) #top, right, bottom, and left margins)
########## LONG FORMAT OF COMMUNITY PRODUCTIVITY
community_prod_plot_long <-
ggplot(dplyr::filter(metadata, year == "2015" & fraction %in% c("Free", "Particle")),
aes(x = lakesite, y = frac_bacprod, fill = fraction)) +
geom_bar(stat = "identity", color = "black", position=position_dodge()) +
geom_errorbar(aes(ymin = frac_bacprod - SD_frac_bacprod, ymax = frac_bacprod + SD_frac_bacprod),
position = position_dodge(width = 0.75), width = 0.25) +
facet_grid(season ~., scales = "free") +
scale_y_continuous(expand = c(0,0), limits = c(0, 80),
breaks = seq(from = 0, to = 80, by = 15)) +
scale_fill_manual(values = fraction_colors) +
guides(fill=guide_legend(nrow=1,byrow=TRUE)) +
xlab("Lake Station") + ylab("Community Production\n(ug C/L/day)") +
theme(legend.position = "bottom", legend.title = element_blank(),
plot.margin = unit(c(0.25, 0.25, 0.25, 0.25), "cm")) #top, right, bottom, and left margins)
########## LONG FORMAT OF CELLULAR PRODUCTIVITY
percapita_prod_plot_long <-
ggplot(dplyr::filter(metadata, year == "2015" & fraction %in% c("Free", "Particle")),
aes(x = lakesite, y = fracprod_per_cell_noinf, fill = fraction)) +
geom_bar(stat = "identity", color = "black", position=position_dodge()) +
facet_grid(season ~ ., scales = "free_x") +
scale_y_continuous(expand = c(0,0)) +
scale_fill_manual(values = fraction_colors) +
guides(fill=guide_legend(nrow=1,byrow=TRUE)) +
xlab("Lake Station") + ylab("Per-Capita Production \n(ug C/cell/day)") +
theme(axis.text.x = element_text(angle = 30, vjust = 1, hjust = 1),
legend.position = "bottom", legend.title = element_blank(),
plot.margin = unit(c(0.25, 0.25, 0.25, 0.5), "cm")) #top, right, bottom, and left margins)
plot_grid(community_prod_plot_long, percapita_prod_plot_long,
labels = c("A", "B"),
nrow = 1, ncol = 2,
align = "v")
plot_grid(prod1, prod2, labels = c("A", "B"),
rel_heights = c(1, 1.3),
nrow = 2, ncol = 1,
align = "v")
### BY STATION
prod_by_station <- ggplot(dplyr::filter(metadata, year == "2015" & fraction %in% c("Free")),
aes(x = lakesite, y = tot_bacprod)) +
geom_boxplot(fill = "grey") + geom_point(size = 3.5, position = position_jitter()) +
scale_y_continuous(expand = c(0,0), limits = c(0,100)) +
scale_fill_manual(values = fraction_colors) +
guides(fill=guide_legend(nrow=1,byrow=TRUE)) +
xlab("Lake Station") + ylab("Total Production (ug C/cell/day)") +
theme(axis.text.x = element_text(angle = 30, vjust = 1, hjust = 1),
legend.position = c(0.85, 0.9), legend.title = element_blank(),
plot.margin = unit(c(0.25, 0.25, 0.25, 0.5), "cm")) #top, right, bottom, and left margins)
prod_by_season <- ggplot(dplyr::filter(metadata, year == "2015" & fraction %in% c("Free")),
aes(x = season, y = tot_bacprod)) +
geom_boxplot(fill = "grey") + geom_point(size = 3.5, position = position_jitter()) +
scale_y_continuous(expand = c(0,0), limits = c(0,100)) +
scale_fill_manual(values = fraction_colors) +
guides(fill=guide_legend(nrow=1,byrow=TRUE)) +
xlab("Season") +
theme(axis.text.x = element_text(angle = 30, vjust = 1, hjust = 1),
axis.title.y = element_blank(),
legend.position = c(0.85, 0.9), legend.title = element_blank(),
plot.margin = unit(c(0.25, 0.25, 0.25, 0.5), "cm")) #top, right, bottom, and left margins)
plot_grid(prod_by_station, prod_by_season, labels = c("A", "B"),
rel_widths = c(1, 0.75),
nrow = 1, ncol = 2,
align = "h")
scaled_surface_PAFLA_pruned_rm2 <- scale_reads(surface_PAFL_otu_pruned_notree_rm2)
set.seed(777)
# Calculate the SOREN distance
soren_whole_dist <- ordinate(
physeq = scaled_surface_PAFLA_pruned_rm2,
method = "PCoA",
distance = "bray",
binary = TRUE) # Make it presence/absence
p1 <- plot_ordination(
physeq = scaled_surface_PAFLA_pruned_rm2,
ordination = soren_whole_dist,
color = "fraction",
shape = "season",
title = "Sørensen") +
geom_point(size=5, alpha = 0.7, aes(fill =fraction, color = fraction)) +
scale_colour_manual(values = fraction_colors) +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
theme(legend.position = "none")
# Calculate the BRAY-CURTIS distance
bray_whole_dist <- ordinate(
physeq = scaled_surface_PAFLA_pruned_rm2,
method = "PCoA",
distance = "bray",
binary = FALSE) # Make it Abundance weighted
p2 <- plot_ordination(
physeq = scaled_surface_PAFLA_pruned_rm2,
ordination = bray_whole_dist ,
color = "fraction",
shape = "season",
title = "Bray-Curtis") +
geom_point(size=5, alpha = 0.7, aes(fill =fraction, color = fraction)) +
scale_colour_manual(values = fraction_colors) +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
theme(legend.position = "bottom", legend.title = element_blank())
p_lakesite <-
plot_ordination(
axes = 2:3,
physeq = scaled_surface_PAFLA_pruned_rm2,
ordination = bray_whole_dist ,
color = "fraction",
shape = "lakesite",
title = "Bray-Curtis") +
geom_point(size=5, alpha = 0.7, aes(fill =fraction, color = fraction)) +
scale_colour_manual(values = fraction_colors) +
scale_size_manual(values = c(3.5, 3.5, 3.5, 3.5)) +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = lakesite_shapes) +
theme(legend.position = "bottom", legend.title = element_blank())
### PCOA FIGURE
pcoa_fig_row1 <- plot_grid(p1,
p2 + theme(legend.position = "none"),
labels = c("A", "B"), align = "h", ncol = 2,
rel_widths = c(1, 1))
season_legend <- get_legend(p2)
lakesite_legend <- get_legend(p_lakesite)
plot_grid(pcoa_fig_row1, season_legend,
nrow = 2, ncol = 1,
rel_heights = c(1, 0.05))
# Calculate bray curtis distance matrix
musk_bray <- phyloseq::distance(scaled_surface_PAFLA_pruned_rm2, method = "bray")
# make a data frame from the sample_data
sampledf <- data.frame(sample_data(scaled_surface_PAFLA_pruned_rm2))
# Adonis test
adonis(musk_bray ~ fraction + season , data = sampledf)##
## Call:
## adonis(formula = musk_bray ~ fraction + season, data = sampledf)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## fraction 1 1.3903 1.39032 16.2279 0.30094 0.001 ***
## season 2 1.5161 0.75804 8.8479 0.32816 0.001 ***
## Residuals 20 1.7135 0.08567 0.37089
## Total 23 4.6199 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Environmental Variables Pairs
# Subset the environmental data only
environmental_data <- metadata %>%
dplyr::select(Temp_C:DO_percent, -BGA_cellspermL, -SRP_ugperL, fraction, norep_filter_name, -DO_percent) %>%
dplyr::filter(fraction == "Free") %>%
dplyr::select(-fraction) %>%
tibble::column_to_rownames(var = "norep_filter_name") %>%
dplyr::rename(Temp = Temp_C,
SpCond = SpCond_uSpercm,
TDS = TDS_mgperL,
ORP = ORP_mV,
Chla = Chl_Lab_ugperL,
Cl = Cl_mgperL,
SO4 = SO4_mgperL,
NO3 = NO3_mgperL,
NH3 = NH3_mgperL,
TKN = TKN_mgperL,
TP = TP_ugperL,
Alk = Alk_mgperL,
DO = DO_mgperL) %>%
as.matrix()
pairs(environmental_data)
Euclidian PCA with Environmental Variables
# Scale the data so their variances are the same!
scaled_enviro <- scale(environmental_data)
# Sanity Checks: check that we get mean of 0 and sd of 1
apply(scaled_enviro, 2, mean)## Temp SpCond TDS pH ORP Chla Cl SO4 NO3 NH3 TKN TP Alk DO
## -3.151262e-16 -1.170392e-15 -8.881784e-16 -2.063155e-15 4.614635e-18 2.081668e-17 -2.937375e-16 3.932311e-16 1.237526e-16 -1.155579e-17 7.400583e-17 -5.090894e-17 1.454855e-15 7.401939e-17apply(scaled_enviro, 2, sd)## Temp SpCond TDS pH ORP Chla Cl SO4 NO3 NH3 TKN TP Alk DO
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1# Run a principal component analysis via the vegan package
pca_environ <- rda(scaled_enviro) #
par(mar = c(5,5,2,5))
plot(summary(pca_environ)$cont$importance[2,]*100,
xlab = "PCA Axis",
ylab = "Variation Explained Per Axis",
ylim = c(0, 105),
col = "cornflowerblue",
cex =2,
pch = 16)
par(new = T)
plot(summary(pca_environ)$cont$importance[3,]*100,
cex = 2,
pch = 17,
ylim = c(0, 105),
col = "firebrick3",
axes=F,
xlab=NA,
ylab=NA)
axis(side = 4)
mtext(side = 4, line = 3, "Total Accumulated Variation")
legend("right",
legend=c("Per Axis", "Total"),
pch=c(16, 17), col=c("cornflowerblue", "firebrick3"))
Linear Models with Environmental Variables
pca_scores_df <- summary(pca_environ)$sites %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "norep_filter_name") %>%
mutate(norep_water_name = paste(substr(norep_filter_name, 1, 4), substr(norep_filter_name, 6, 8), sep = "")) %>%
dplyr::select(-norep_filter_name)
metadata_pca <- metadata %>%
mutate(norep_water_name = paste(substr(norep_filter_name, 1, 4), substr(norep_filter_name, 6, 8), sep = "")) %>%
left_join(pca_scores_df, by = "norep_water_name")
##### SINGLE REGRESSION with PC1
## Free living
comm_lm_PC1_free <- summary(lm(frac_bacprod ~ PC1,
data = filter(metadata_pca, fraction == "Free")))
## Particle-associated
comm_lm_PC1_part <- summary(lm(frac_bacprod ~ PC1,
data = filter(metadata_pca, fraction == "Particle")))
## PER CAPITA: Free living
percap_lm_PC1_free <- summary(lm(log10(fracprod_per_cell_noinf) ~ PC1,
data = filter(metadata_pca, fraction == "Free")))
## PER CAPITA: Particle-associated
percap_lm_PC1_part <- summary(lm(log10(fracprod_per_cell_noinf) ~ PC1,
data = filter(metadata_pca, fraction == "Particle")))
# Put the results all together
pca_lm_row1 <- c("Free", "Per-Liter","PC1",
round(comm_lm_PC1_free$adj.r.squared, digits = 3),
round(comm_lm_PC1_free$coefficients[2,4], digits = 3))
pca_lm_row2 <- c("Particle", "Per-Liter","PC1",
round(comm_lm_PC1_part$adj.r.squared, digits = 3),
round(comm_lm_PC1_part$coefficients[2,4], digits = 3))
pca_lm_row3 <- c("Free", "Per-Capita","PC1",
round(percap_lm_PC1_free$adj.r.squared, digits = 3),
round(percap_lm_PC1_free$coefficients[2,4], digits = 3))
pca_lm_row4 <- c("Particle", "Per-Capita", "PC1",
round(percap_lm_PC1_part$adj.r.squared, digits = 3),
round(percap_lm_PC1_part$coefficients[2,4], digits = 3))
pca_lm_results_df <-
rbind(pca_lm_row1, pca_lm_row2, pca_lm_row3, pca_lm_row4)
colnames(pca_lm_results_df) <- c("fraction", "Predictor","Prod_measure", "Adjusted_R2", "p-value")
row.names(pca_lm_results_df) = NULL
#pander(pca_lm_results_df,
# caption = "Single Linear regression statistics for PC1.")
##### SINGLE REGRESSION with PC2
## Free living
comm_lm_PC2_free <- summary(lm(frac_bacprod ~ PC2,
data = filter(metadata_pca, fraction == "Free")))
## Particle-associated
comm_lm_PC2_part <- summary(lm(frac_bacprod ~ PC2,
data = filter(metadata_pca, fraction == "Particle")))
## PER CAPITA: Free living
percap_lm_PC2_free <- summary(lm(log10(fracprod_per_cell_noinf) ~ PC2,
data = filter(metadata_pca, fraction == "Free")))
## PER CAPITA: Particle-associated
percap_lm_PC2_part <- summary(lm(log10(fracprod_per_cell_noinf) ~ PC2,
data = filter(metadata_pca, fraction == "Particle")))
# Put the results all together
pca_lm2_row1 <- c("Free", "Per-Liter","PC2",
round(comm_lm_PC2_free$adj.r.squared, digits = 3),
round(comm_lm_PC2_free$coefficients[2,4], digits = 3))
pca_lm2_row2 <- c("Particle", "Per-Liter","PC2",
round(comm_lm_PC2_part$adj.r.squared, digits = 3),
round(comm_lm_PC2_part$coefficients[2,4], digits = 3))
pca_lm2_row3 <- c("Free", "Per-Capita","PC2",
round(percap_lm_PC2_free$adj.r.squared, digits = 3),
round(percap_lm_PC2_free$coefficients[2,4], digits = 3))
pca_lm2_row4 <- c("Particle", "Per-Capita", "PC2",
round(percap_lm_PC2_part$adj.r.squared, digits = 3),
round(percap_lm_PC2_part$coefficients[2,4], digits = 3))
pca_lm2_results_df <-
rbind(pca_lm2_row1, pca_lm2_row2, pca_lm2_row3, pca_lm2_row4)
colnames(pca_lm2_results_df) <- c("fraction", "Predictor","Prod_measure", "Adjusted_R2", "p-value")
row.names(pca_lm2_results_df) = NULL
#pander(pca_lm2_results_df,
# caption = "Single Linear regression statistics for PC2.")
##### SINGLE REGRESSION with PC3
## Free living
comm_lm_PC3_free <- summary(lm(frac_bacprod ~ PC3,
data = filter(metadata_pca, fraction == "Free")))
## Particle-associated
comm_lm_PC3_part <- summary(lm(frac_bacprod ~ PC3,
data = filter(metadata_pca, fraction == "Particle")))
## PER CAPITA: Free living
percap_lm_PC3_free <- summary(lm(log10(fracprod_per_cell_noinf) ~ PC3,
data = filter(metadata_pca, fraction == "Free")))
## PER CAPITA: Particle-associated
percap_lm_PC3_part <- summary(lm(log10(fracprod_per_cell_noinf) ~ PC3,
data = filter(metadata_pca, fraction == "Particle")))
# Put the results all together
pca_lm_row1_PC3 <- c("Free", "Per-Liter","PC3",
round(comm_lm_PC3_free$adj.r.squared, digits = 3),
round(comm_lm_PC3_free$coefficients[2,4], digits = 3))
pca_lm_row2_PC3 <- c("Particle", "Per-Liter","PC3",
round(comm_lm_PC3_part$adj.r.squared, digits = 3),
round(comm_lm_PC3_part$coefficients[2,4], digits = 3))
pca_lm_row3_PC3 <- c("Free", "Per-Capita","PC3",
round(percap_lm_PC3_free$adj.r.squared, digits = 3),
round(percap_lm_PC3_free$coefficients[2,4], digits = 3))
pca_lm_row4_PC3 <- c("Particle", "Per-Capita", "PC3",
round(percap_lm_PC3_part$adj.r.squared, digits = 3),
round(percap_lm_PC3_part$coefficients[2,4], digits = 3))
pca_lm_results_df_PC3 <-
rbind(pca_lm_row1_PC3, pca_lm_row2_PC3, pca_lm_row3_PC3, pca_lm_row4_PC3)
colnames(pca_lm_results_df_PC3) <- c("fraction", "Predictor","Prod_measure", "Adjusted_R2", "p-value")
row.names(pca_lm_results_df_PC3) = NULL
#pander(pca_lm_results_df_PC3,
# caption = "Single Linear regression statistics for PC3.")
##### SINGLE REGRESSION with PC4
## Free living
comm_lm_PC4_free <- summary(lm(frac_bacprod ~ PC4,
data = filter(metadata_pca, fraction == "Free")))
## Particle-associated
comm_lm_PC4_part <- summary(lm(frac_bacprod ~ PC4,
data = filter(metadata_pca, fraction == "Particle")))
## PER CAPITA: Free living
percap_lm_PC4_free <- summary(lm(log10(fracprod_per_cell_noinf) ~ PC4,
data = filter(metadata_pca, fraction == "Free")))
## PER CAPITA: Particle-associated
percap_lm_PC4_part <- summary(lm(log10(fracprod_per_cell_noinf) ~ PC4,
data = filter(metadata_pca, fraction == "Particle")))
# Put the results all together
pca_lm_row1_PC4 <- c("Free", "Per-Liter","PC4",
round(comm_lm_PC4_free$adj.r.squared, digits = 3),
round(comm_lm_PC4_free$coefficients[2,4], digits = 3))
pca_lm_row2_PC4 <- c("Particle", "Per-Liter","PC4",
round(comm_lm_PC4_part$adj.r.squared, digits = 3),
round(comm_lm_PC4_part$coefficients[2,4], digits = 3))
pca_lm_row3_PC4 <- c("Free", "Per-Capita","PC4",
round(percap_lm_PC4_free$adj.r.squared, digits = 3),
round(percap_lm_PC4_free$coefficients[2,4], digits = 3))
pca_lm_row4_PC4 <- c("Particle", "Per-Capita", "PC4",
round(percap_lm_PC4_part$adj.r.squared, digits = 3),
round(percap_lm_PC4_part$coefficients[2,4], digits = 3))
pca_lm_results_df_PC4 <-
rbind(pca_lm_row1_PC4, pca_lm_row2_PC4, pca_lm_row3_PC4, pca_lm_row4_PC4)
colnames(pca_lm_results_df_PC4) <- c("fraction", "Predictor","Prod_measure", "Adjusted_R2", "p-value")
row.names(pca_lm_results_df_PC4) = NULL
#pander(pca_lm_results_df_PC4,
# caption = "Single Linear regression statistics for PC4.")
##### SINGLE REGRESSION with PC5
## Free living
comm_lm_PC5_free <- summary(lm(frac_bacprod ~ PC5,
data = filter(metadata_pca, fraction == "Free")))
## Particle-associated
comm_lm_PC5_part <- summary(lm(frac_bacprod ~ PC5,
data = filter(metadata_pca, fraction == "Particle")))
## PER CAPITA: Free living
percap_lm_PC5_free <- summary(lm(log10(fracprod_per_cell_noinf) ~ PC5,
data = filter(metadata_pca, fraction == "Free")))
## PER CAPITA: Particle-associated
percap_lm_PC5_part <- summary(lm(log10(fracprod_per_cell_noinf) ~ PC5,
data = filter(metadata_pca, fraction == "Particle")))
# Put the results all together
pca_lm_row1_PC5 <- c("Free", "Per-Liter","PC5",
round(comm_lm_PC5_free$adj.r.squared, digits = 3),
round(comm_lm_PC5_free$coefficients[2,4], digits = 3))
pca_lm_row2_PC5 <- c("Particle", "Per-Liter","PC5",
round(comm_lm_PC5_part$adj.r.squared, digits = 3),
round(comm_lm_PC5_part$coefficients[2,4], digits = 3))
pca_lm_row3_PC5 <- c("Free", "Per-Capita","PC5",
round(percap_lm_PC5_free$adj.r.squared, digits = 3),
round(percap_lm_PC5_free$coefficients[2,4], digits = 3))
pca_lm_row4_PC5 <- c("Particle", "Per-Capita", "PC5",
round(percap_lm_PC5_part$adj.r.squared, digits = 3),
round(percap_lm_PC5_part$coefficients[2,4], digits = 3))
pca_lm_results_df_PC5 <-
rbind(pca_lm_row1_PC5, pca_lm_row2_PC5, pca_lm_row3_PC5, pca_lm_row4_PC5)
colnames(pca_lm_results_df_PC5) <- c("fraction", "Predictor","Prod_measure", "Adjusted_R2", "p-value")
row.names(pca_lm_results_df_PC5) = NULL
#pander(pca_lm_results_df_PC5,
# caption = "Single Linear regression statistics for PC5.")
##### SINGLE REGRESSION with PC6
## Free living
comm_lm_PC6_free <- summary(lm(frac_bacprod ~ PC6,
data = filter(metadata_pca, fraction == "Free")))
## Particle-associated
comm_lm_PC6_part <- summary(lm(frac_bacprod ~ PC6,
data = filter(metadata_pca, fraction == "Particle")))
## PER CAPITA: Free living
percap_lm_PC6_free <- summary(lm(log10(fracprod_per_cell_noinf) ~ PC6,
data = filter(metadata_pca, fraction == "Free")))
## PER CAPITA: Particle-associated
percap_lm_PC6_part <- summary(lm(log10(fracprod_per_cell_noinf) ~ PC6,
data = filter(metadata_pca, fraction == "Particle")))
# Put the results all together
pca_lm_row1_PC6 <- c("Free", "Per-Liter","PC6",
round(comm_lm_PC6_free$adj.r.squared, digits = 3),
round(comm_lm_PC6_free$coefficients[2,4], digits = 3))
pca_lm_row2_PC6 <- c("Particle", "Per-Liter","PC6",
round(comm_lm_PC6_part$adj.r.squared, digits = 3),
round(comm_lm_PC6_part$coefficients[2,4], digits = 3))
pca_lm_row3_PC6 <- c("Free", "Per-Capita","PC6",
round(percap_lm_PC6_free$adj.r.squared, digits = 3),
round(percap_lm_PC6_free$coefficients[2,4], digits = 3))
pca_lm_row4_PC6 <- c("Particle", "Per-Capita", "PC6",
round(percap_lm_PC6_part$adj.r.squared, digits = 3),
round(percap_lm_PC6_part$coefficients[2,4], digits = 3))
pca_lm_results_df_PC6 <-
rbind(pca_lm_row1_PC6, pca_lm_row2_PC6, pca_lm_row3_PC6, pca_lm_row4_PC6)
colnames(pca_lm_results_df_PC6) <- c("fraction", "Predictor","Prod_measure", "Adjusted_R2", "p-value")
row.names(pca_lm_results_df_PC6) = NULL
#pander(pca_lm_results_df_PC6,
# caption = "Single Linear regression statistics for PC6.")
##### MULTIPLE REGRESSION
## Free living
summary(lm(frac_bacprod ~ PC1 + PC2,
data = filter(metadata_pca, fraction == "Free")))##
## Call:
## lm(formula = frac_bacprod ~ PC1 + PC2, data = filter(metadata_pca,
## fraction == "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.054 -9.327 -7.554 10.981 29.901
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.058 4.954 4.857 0.0009 ***
## PC1 6.295 4.871 1.292 0.2284
## PC2 -4.385 4.871 -0.900 0.3914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.16 on 9 degrees of freedom
## Multiple R-squared: 0.2161, Adjusted R-squared: 0.04186
## F-statistic: 1.24 on 2 and 9 DF, p-value: 0.3344## Particle-associated
summary(lm(frac_bacprod ~ PC1 + PC2,
data = filter(metadata_pca, fraction == "Particle")))##
## Call:
## lm(formula = frac_bacprod ~ PC1 + PC2, data = filter(metadata_pca,
## fraction == "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.481 -3.757 0.410 3.213 15.482
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.958 2.129 4.678 0.00115 **
## PC1 1.107 2.093 0.529 0.60954
## PC2 -4.424 2.093 -2.114 0.06368 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.373 on 9 degrees of freedom
## Multiple R-squared: 0.3454, Adjusted R-squared: 0.1999
## F-statistic: 2.374 on 2 and 9 DF, p-value: 0.1486## PER CAPITA: Free living
summary(lm(log10(fracprod_per_cell_noinf) ~ PC1 + PC2,
data = filter(metadata_pca, fraction == "Free")))##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ PC1 + PC2, data = filter(metadata_pca,
## fraction == "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5064 -0.2177 -0.1287 0.2483 0.4932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.5750 0.1047 -72.357 9.3e-14 ***
## PC1 0.1199 0.1029 1.165 0.274
## PC2 -0.1679 0.1029 -1.631 0.137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3627 on 9 degrees of freedom
## Multiple R-squared: 0.3087, Adjusted R-squared: 0.1551
## F-statistic: 2.009 on 2 and 9 DF, p-value: 0.1899## PER CAPITA: Particle-associated
summary(lm(log10(fracprod_per_cell_noinf) ~ PC1 + PC2,
data = filter(metadata_pca, fraction == "Particle")))##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ PC1 + PC2, data = filter(metadata_pca,
## fraction == "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.53476 -0.15558 -0.00926 0.08264 0.65269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.6657 0.1074 -62.056 5.05e-12 ***
## PC1 0.2026 0.1060 1.912 0.09229 .
## PC2 -0.3818 0.1075 -3.553 0.00747 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3522 on 8 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.6498, Adjusted R-squared: 0.5623
## F-statistic: 7.423 on 2 and 8 DF, p-value: 0.01504PC1_lm_plot <-
ggplot(metadata_pca, aes(x = PC1, y = log10(fracprod_per_cell_noinf),
color = fraction, fill = fraction)) +
geom_point() + ylab("log10(cellular production)") +
scale_color_manual(values = fraction_colors) +
scale_fill_manual(values = fraction_colors) +
geom_smooth(method = "lm") +
theme(legend.title = element_blank(), legend.position = "bottom")
PC2_lm_plot <-
ggplot(metadata_pca, aes(x = PC2, y = log10(fracprod_per_cell_noinf),
color = fraction, fill = fraction)) +
geom_point() + ylab("log10(cellular production)") +
scale_color_manual(values = fraction_colors) +
scale_fill_manual(values = fraction_colors) +
geom_smooth(method = "lm") +
theme(legend.title = element_blank(), legend.position = "bottom")
plot_grid(PC2_lm_plot, PC2_lm_plot,
labels = c("A", "B"),
nrow = 1, ncol = 2)
Filtered PCA Linear Model Results
Below represents the significant LM results from the individual PCA axes:
all_pca_lm_results <- dplyr::bind_rows(
data.frame(pca_lm_results_df),
data.frame(pca_lm2_results_df),
data.frame(pca_lm_results_df_PC3),
data.frame(pca_lm_results_df_PC4),
data.frame(pca_lm_results_df_PC5),
data.frame(pca_lm_results_df_PC6))
pander(all_pca_lm_results,
caption = "All individual LM results from PCA axes.")| fraction | Predictor | Prod_measure | Adjusted_R2 | p.value |
|---|---|---|---|---|
| Free | Per-Liter | PC1 | 0.06 | 0.221 |
| Particle | Per-Liter | PC1 | -0.078 | 0.658 |
| Free | Per-Capita | PC1 | 0.015 | 0.306 |
| Particle | Per-Capita | PC1 | -0.003 | 0.351 |
| Free | Per-Liter | PC2 | -0.022 | 0.404 |
| Particle | Per-Liter | PC2 | 0.257 | 0.053 |
| Free | Per-Capita | PC2 | 0.125 | 0.14 |
| Particle | Per-Capita | PC2 | 0.433 | 0.016 |
| Free | Per-Liter | PC3 | 0.121 | 0.144 |
| Particle | Per-Liter | PC3 | -0.05 | 0.507 |
| Free | Per-Capita | PC3 | 0.188 | 0.089 |
| Particle | Per-Capita | PC3 | -0.103 | 0.799 |
| Free | Per-Liter | PC4 | -0.068 | 0.595 |
| Particle | Per-Liter | PC4 | -0.027 | 0.418 |
| Free | Per-Capita | PC4 | 0.124 | 0.141 |
| Particle | Per-Capita | PC4 | -0.059 | 0.524 |
| Free | Per-Liter | PC5 | 0.242 | 0.06 |
| Particle | Per-Liter | PC5 | -0.005 | 0.353 |
| Free | Per-Capita | PC5 | 0.035 | 0.263 |
| Particle | Per-Capita | PC5 | 0.06 | 0.233 |
| Free | Per-Liter | PC6 | -0.095 | 0.83 |
| Particle | Per-Liter | PC6 | 0.112 | 0.154 |
| Free | Per-Capita | PC6 | -0.058 | 0.544 |
| Particle | Per-Capita | PC6 | 0.089 | 0.193 |
pander(dplyr::filter(all_pca_lm_results, p.value < 0.06),
caption = "Individual Linear Model results from the individual PCA axes with a p-value less than 0.06.")| fraction | Predictor | Prod_measure | Adjusted_R2 | p.value |
|---|---|---|---|---|
| Particle | Per-Liter | PC2 | 0.257 | 0.053 |
| Particle | Per-Capita | PC2 | 0.433 | 0.016 |
# Multiple regression with community-wide production
summary(lm(frac_bacprod ~ PC1 + PC2, data = filter(metadata_pca, fraction == "Particle")))##
## Call:
## lm(formula = frac_bacprod ~ PC1 + PC2, data = filter(metadata_pca,
## fraction == "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.481 -3.757 0.410 3.213 15.482
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.958 2.129 4.678 0.00115 **
## PC1 1.107 2.093 0.529 0.60954
## PC2 -4.424 2.093 -2.114 0.06368 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.373 on 9 degrees of freedom
## Multiple R-squared: 0.3454, Adjusted R-squared: 0.1999
## F-statistic: 2.374 on 2 and 9 DF, p-value: 0.1486# Multiple regression with per-capita production
summary(lm(log10(fracprod_per_cell_noinf) ~ PC1 + PC2, data = filter(metadata_pca, fraction == "Free")))##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ PC1 + PC2, data = filter(metadata_pca,
## fraction == "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5064 -0.2177 -0.1287 0.2483 0.4932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.5750 0.1047 -72.357 9.3e-14 ***
## PC1 0.1199 0.1029 1.165 0.274
## PC2 -0.1679 0.1029 -1.631 0.137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3627 on 9 degrees of freedom
## Multiple R-squared: 0.3087, Adjusted R-squared: 0.1551
## F-statistic: 2.009 on 2 and 9 DF, p-value: 0.1899par(oma(0.1, 0.1, 0.1, 0.1))## Error in oma(0.1, 0.1, 0.1, 0.1): could not find function "oma"biplot(pca_environ,
xlab = paste("PC1", paste(round(summary(pca_environ)$cont$importance[2,1]*100, digits = 2), "%", sep = ""), sep = ": "),
ylab = paste("PC2", paste(round(summary(pca_environ)$cont$importance[2,2]*100, digits = 2), "%", sep = ""), sep = ": "))
ggplot(filter(metadata, norep_filter_name != "MOTEJ515"),
aes(y = log10(as.numeric(fraction_bac_abund)), x = fraction)) +
geom_jitter(size = 3, aes(fill = fraction, shape = season), width = 0.2) +
geom_boxplot(alpha = 0.5, outlier.shape = NA, aes( fill = fraction)) +
ylab("Log10(Bacterial Counts) \n (cells/mL)") +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
##### Particle vs free cell abundances
geom_path(data = dat1, aes(x = a, y = b), linetype = 1, color = "gray40") +
annotate("text", x=1.5, y=6.5, fontface = "bold", size = 3.5, color = "gray40",
label= paste("***\np =", round(frac_abund_wilcox$p.value, digits = 6))) +
theme(legend.position = "bottom",
legend.title = element_blank(),
axis.title.x = element_blank())## Error in fortify(data): object 'dat1' not foundFigure 1
######################################################### Fraction ABUNDANCe
frac_abund_wilcox <- wilcox.test(log10(as.numeric(fraction_bac_abund)) ~ fraction, data = metadata)
frac_abund_wilcox##
## Wilcoxon rank sum test
##
## data: log10(as.numeric(fraction_bac_abund)) by fraction
## W = 0, p-value = 1.479e-06
## alternative hypothesis: true location shift is not equal to 0metadata %>%
group_by(fraction) %>%
summarize(mean(as.numeric(fraction_bac_abund)))## # A tibble: 2 x 2
## fraction `mean(as.numeric(fraction_bac_abund))`
## <fctr> <dbl>
## 1 Particle 41168.88
## 2 Free 734522.25# Make a data frame to draw significance line in boxplot (visually calculated)
dat1 <- data.frame(a = c(1.15,1.15,1.85,1.85), b = c(6.45,6.5,6.5,6.45)) # WholePart vs WholeFree
poster_a <- ggplot(filter(metadata, norep_filter_name != "MOTEJ515"),
aes(y = log10(as.numeric(fraction_bac_abund)), x = fraction)) +
geom_jitter(size = 3, aes(fill = fraction, shape = season), width = 0.2) +
geom_boxplot(alpha = 0.5, outlier.shape = NA, aes( fill = fraction)) +
ylab("Log10(Bacterial Counts) \n (cells/mL)") +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
##### Particle vs free cell abundances
geom_path(data = dat1, aes(x = a, y = b), linetype = 1, color = "gray40") +
annotate("text", x=1.5, y=6.5, fontface = "bold", size = 3.5, color = "gray40",
label= paste("***\np =", round(frac_abund_wilcox$p.value, digits = 6))) +
theme(legend.position = "bottom",
legend.title = element_blank(),
axis.title.x = element_blank())
######################################################### TOTAL PRODUCTION
totprod_wilcox <- wilcox.test(frac_bacprod ~ fraction, data = metadata)
totprod_wilcox##
## Wilcoxon rank sum test
##
## data: frac_bacprod by fraction
## W = 33, p-value = 0.02418
## alternative hypothesis: true location shift is not equal to 0metadata %>%
group_by(fraction) %>%
summarize(mean(frac_bacprod))## # A tibble: 2 x 2
## fraction `mean(frac_bacprod)`
## <fctr> <dbl>
## 1 Particle 9.958333
## 2 Free 24.058333# Make a data frame to draw significance line in boxplot (visually calculated)
dat2 <- data.frame(a = c(1.15,1.15,1.85,1.85), b = c(71,72,72,71)) # WholePart vs WholeFree
poster_b <- ggplot(metadata, aes(y = frac_bacprod, x = fraction)) +
geom_jitter(size = 3, aes(fill = fraction, shape = season), width = 0.2) +
geom_boxplot(alpha = 0.5, outlier.shape = NA, aes( fill = fraction)) +
scale_fill_manual(values = fraction_colors, guide = FALSE) +
scale_shape_manual(values = season_shapes) +
scale_y_continuous(limits = c(0, 73)) +
ylab("Community Production \n (μgC/L/day)") +
##### Particle vs free bulk production
geom_path(data = dat2, aes(x = a, y = b), linetype = 1, color = "gray40") +
annotate("text", x=1.5, y=72, fontface = "bold", size = 3.5, color = "gray40",
label= paste("**\np =", round(totprod_wilcox$p.value, digits = 3))) +
theme(legend.position = "none",
axis.title.x = element_blank())
######################################################### TOTAL PRODUCTION
percellprod_wilcox <- wilcox.test(log10(fracprod_per_cell) ~ fraction, data = filter(metadata, norep_filter_name != "MOTEJ515"))
percellprod_wilcox##
## Wilcoxon rank sum test
##
## data: log10(fracprod_per_cell) by fraction
## W = 125, p-value = 6.656e-05
## alternative hypothesis: true location shift is not equal to 0filter(metadata, norep_filter_name != "MOTEJ515") %>%
group_by(fraction) %>%
summarize(mean(fracprod_per_cell))## # A tibble: 2 x 2
## fraction `mean(fracprod_per_cell)`
## <fctr> <dbl>
## 1 Particle 4.816116e-07
## 2 Free 3.866798e-08# Make a data frame to draw significance line in boxplot (visually calculated)
dat3 <- data.frame(a = c(1.15,1.15,1.85,1.85), b = c(-5.05,-5,-5,-5.05)) # WholePart vs WholeFree
poster_c <- ggplot(filter(metadata, norep_filter_name != "MOTEJ515"),
aes(y = log10(fracprod_per_cell), x = fraction)) +
geom_jitter(size = 3, aes(fill = fraction, shape = season), width = 0.2) +
geom_boxplot(alpha = 0.5, outlier.shape = NA, aes( fill = fraction)) +
scale_fill_manual(values = fraction_colors, guide = FALSE) +
scale_shape_manual(values = season_shapes) +
ylim(c(-8.5, -5)) +
ylab("Log10(Per-Capita Production) \n(μgC/cell/day)") +
##### Particle vs free per-cell production
geom_path(data = dat3, aes(x = a, y = b), linetype = 1, color = "gray40") +
annotate("text", x=1.5, y=-5, fontface = "bold", size = 3.5, color = "gray40",
label= paste("***\np =", round(percellprod_wilcox$p.value, digits = 5))) +
theme(legend.position = "none",
axis.title.x = element_blank())
######## FIGURE 1
# legend
legend <- get_legend(poster_a)
row1_plots <- plot_grid(poster_a + theme(legend.position = "none"), poster_b, poster_c,
labels = c("A", "B", "C"),
ncol = 3, nrow = 1)
#fig_1 <-
plot_grid(row1_plots, legend,
ncol = 1, nrow = 2,
rel_heights = c(1, 0.05))
Figure S1
work_df <- metadata %>%
dplyr::select(norep_filter_name, fraction, fraction_bac_abund, frac_bacprod, fracprod_per_cell_noinf) %>%
mutate(norep_water_name = paste(substr(norep_filter_name, 1,4), substr(norep_filter_name, 6,9), sep = "")) %>%
dplyr::select(-norep_filter_name)
part_work_df <- work_df %>%
filter(fraction == "Particle") %>%
rename(part_bacabund = fraction_bac_abund,
part_prod = frac_bacprod,
part_percell_prod = fracprod_per_cell_noinf) %>%
dplyr::select(-fraction)
free_work_df <- work_df %>%
filter(fraction == "Free") %>%
rename(free_bacabund = fraction_bac_abund,
free_prod = frac_bacprod,
free_percell_prod = fracprod_per_cell_noinf) %>%
dplyr::select(-fraction)
byfrac_df <- part_work_df %>%
left_join(free_work_df, by = "norep_water_name")
byfrac_df$season <- substr(byfrac_df$norep_water_name, 5,5) # 7th letter = month sampled
byfrac_df$season <- as.character(byfrac_df$season)
byfrac_df$season <- ifelse(byfrac_df$season == "5", "Spring",
ifelse(byfrac_df$season == "7", "Summer",
ifelse(byfrac_df$season == "9", "Fall",
"NA")))
byfrac_df$season <- factor(byfrac_df$season, levels = c("Spring", "Summer", "Fall"))
summary(lm(log10(part_bacabund) ~ log10(free_bacabund), data = filter(byfrac_df, norep_water_name != "MOTE515")))##
## Call:
## lm(formula = log10(part_bacabund) ~ log10(free_bacabund), data = filter(byfrac_df,
## norep_water_name != "MOTE515"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52690 -0.08048 0.05903 0.19565 0.35255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0900 3.2247 0.648 0.533
## log10(free_bacabund) 0.4264 0.5532 0.771 0.461
##
## Residual standard error: 0.3111 on 9 degrees of freedom
## Multiple R-squared: 0.06194, Adjusted R-squared: -0.04229
## F-statistic: 0.5943 on 1 and 9 DF, p-value: 0.4605plot1 <- ggplot(filter(byfrac_df, norep_water_name != "MOTE515"),
aes(x = log10(free_bacabund), y = log10(part_bacabund))) +
xlab("Free") + ylab("Particle") +
ggtitle("Log10(Bacterial Counts) \n (cells/mL)") +
geom_point(size = 3, fill = "grey", aes(shape = season), width = 0.2) +
scale_shape_manual(values = season_shapes) +
theme(legend.position = "bottom",
legend.title = element_blank())
lm_prod_corr <- lm(part_prod ~ free_prod, data = byfrac_df)
plot2 <- ggplot(byfrac_df, aes(x = free_prod, y = part_prod)) +
xlab("Free") + ylab("Particle") +
ggtitle("Community Production \n(μgC/L/day)") +
geom_point(size = 3, fill = "grey", aes(shape = season), width = 0.2) +
scale_shape_manual(values = season_shapes) +
geom_smooth(method = "lm", color = "black") +
annotate("text", x =20, y= 30, color = "black", fontface = "bold", size = 4,
label = paste("R2 =", round(summary(lm_prod_corr)$adj.r.squared, digits = 3), "\n",
"p =", round(unname(summary(lm_prod_corr)$coefficients[,4][2]), digits = 3))) +
theme(legend.position = "none")
lm_percell_corr <- lm(log10(part_percell_prod) ~ log10(free_percell_prod), data = byfrac_df)
plot3 <- ggplot(byfrac_df,
aes(x = log10(free_percell_prod), y = log10(part_percell_prod))) +
xlab("Free") + ylab("Particle") +
ggtitle("Log10(Cellular Production) \n (μgC/cell/day)") +
geom_point(size = 3, fill = "grey", aes(shape = season), width = 0.2) +
scale_shape_manual(values = season_shapes) +
geom_smooth(method = "lm", color = "black") +
annotate("text", y = -5.8, x=-7.9, color = "black", fontface = "bold", size = 4,
label = paste("R2 =", round(summary(lm_percell_corr)$adj.r.squared, digits = 3), "\n",
"p =", round(unname(summary(lm_percell_corr)$coefficients[,4][2]), digits = 3))) +
theme(legend.position = "none")
legend_s1 <- get_legend(plot1)
top_row_S1 <- plot_grid(plot1 +theme(legend.position = "none"), plot2, plot3,
nrow = 1, ncol = 3,
labels = c("A", "B", "C"),
align = "h")
plot_grid(top_row_S1, legend_s1,
rel_heights = c(1, 0.05),
nrow = 2, ncol = 1)
Calculate Community Diversity
# Set the seed for reproducibility
set.seed(777)
# Calculate the alpha diversity with 100 repsampling events
alpha_calc <- calc_alpha_diversity(physeq = surface_PAFL_otu_pruned_notree_rm2)
# What was the minimum sample size?
min(sample_sums(surface_PAFL_otu_pruned_notree_rm2)) - 1## [1] 6489# Put it altogether in a dataframe
otu_alphadiv <- calc_mean_alphadiv(physeq = surface_PAFL_otu_pruned_notree_rm2,
richness_df = alpha_calc$Richness,
evenness_df = alpha_calc$Inverse_Simpson,
shannon_df = alpha_calc$Shannon) %>%
mutate(fraction = factor(fraction, levels = c("Particle","Free")),
lakesite = factor(lakesite, levels = c("Outlet", "Deep", "Bear", "River")),
measure = factor(measure, levels = c("Richness", "Shannon_Entropy", "Inverse_Simpson", "Simpsons_Evenness")),
norep_water_name = paste(substr(norep_filter_name, 1, 4), substr(norep_filter_name, 6, 8), sep = ""))
otu_alphadiv <- otu_alphadiv %>%
left_join(pca_scores_df, by = "norep_water_name")Correlations between Diversity Measures
##########################################################################
########################### CORRELATIONS #############################
##########################################################################
# RICHNESS vs SHANNON
cor(filter(otu_alphadiv, measure == "Richness" & fraction == "Particle")$mean,
filter(otu_alphadiv, measure == "Shannon_Entropy" & fraction == "Particle")$mean) # YES## [1] 0.9406491# SHANNON VS INVERSE SIMPSON
cor(filter(otu_alphadiv, measure == "Shannon_Entropy" & fraction == "Particle")$mean,
filter(otu_alphadiv, measure == "Inverse_Simpson" & fraction == "Particle")$mean) # YES## [1] 0.9651242# INVERSE SIMPSON VS SIMPSONS EVENNESS
cor(filter(otu_alphadiv, measure == "Inverse_Simpson" & fraction == "Particle")$mean,
filter(otu_alphadiv, measure == "Simpsons_Evenness" & fraction == "Particle")$mean) # YES## [1] 0.9145095# SIMPSONS EVENNESS VS RICHNESS
cor(filter(otu_alphadiv, measure == "Simpsons_Evenness" & fraction == "Particle")$mean,
filter(otu_alphadiv, measure == "Richness" & fraction == "Particle")$mean) # YES## [1] 0.6881205Linear Regression Statistics
#################################### Subset Dataframes
richness <- filter(otu_alphadiv, measure == "Richness")
shannon <- filter(otu_alphadiv, measure == "Shannon_Entropy")
invsimps <- filter(otu_alphadiv, measure == "Inverse_Simpson")
simpseven <- filter(otu_alphadiv, measure == "Simpsons_Evenness")Linear Regressions with Fraction Diversity
#################################### Bulk Measure Production ####################################
################### Richness ###################
summary(lm(frac_bacprod ~ mean, data = richness)) # All samples together##
## Call:
## lm(formula = frac_bacprod ~ mean, data = richness)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.057 -12.017 -5.654 9.256 46.605
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.501168 9.039579 2.047 0.0528 .
## mean -0.003335 0.018911 -0.176 0.8616
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.54 on 22 degrees of freedom
## Multiple R-squared: 0.001412, Adjusted R-squared: -0.04398
## F-statistic: 0.0311 on 1 and 22 DF, p-value: 0.8616# Particle-Associated
lm_prod_vs_rich_PA <- lm(frac_bacprod ~ mean, data = filter(richness, fraction == "Particle"))
summary(lm_prod_vs_rich_PA)##
## Call:
## lm(formula = frac_bacprod ~ mean, data = filter(richness, fraction ==
## "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.1271 -2.8865 -0.4649 3.7537 9.8401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -11.269531 5.717450 -1.971 0.07701 .
## mean 0.038125 0.009868 3.863 0.00314 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.476 on 10 degrees of freedom
## Multiple R-squared: 0.5988, Adjusted R-squared: 0.5587
## F-statistic: 14.93 on 1 and 10 DF, p-value: 0.003143# Cross validate particle-associated for a better estimate of the adjusted R-squared
cv_lm_prod_vs_rich_PA <- train(
frac_bacprod ~ mean, data = filter(richness, fraction == "Particle"),
method ='lm',
trControl = trainControl(method ="repeatedcv", number = 3, repeats = 100),
tuneGrid = expand.grid(intercept = TRUE))
cv_lm_prod_vs_rich_PA$results # Particle-Associated CV results ## intercept RMSE Rsquared RMSESD RsquaredSD
## 1 TRUE 6.658983 0.6537518 2.255394 0.2948377summary(lm(frac_bacprod ~ mean, data = filter(richness, fraction == "Free"))) # Free Living ##
## Call:
## lm(formula = frac_bacprod ~ mean, data = filter(richness, fraction ==
## "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.094 -11.112 -2.755 8.611 33.308
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.40793 21.62006 0.250 0.808
## mean 0.05511 0.06207 0.888 0.395
##
## Residual standard error: 17.7 on 10 degrees of freedom
## Multiple R-squared: 0.07306, Adjusted R-squared: -0.01963
## F-statistic: 0.7882 on 1 and 10 DF, p-value: 0.3955################### Shannon Entropy ###################
summary(lm(frac_bacprod ~ mean, data = shannon)) # All samples together##
## Call:
## lm(formula = frac_bacprod ~ mean, data = shannon)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.659 -11.878 -5.666 9.231 46.593
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.63373 26.71652 0.623 0.540
## mean 0.08797 6.22969 0.014 0.989
##
## Residual standard error: 15.55 on 22 degrees of freedom
## Multiple R-squared: 9.064e-06, Adjusted R-squared: -0.04545
## F-statistic: 0.0001994 on 1 and 22 DF, p-value: 0.9889# Particle-Associated
lm_prod_vs_shannon_PA <- lm(frac_bacprod ~ mean, data = filter(shannon, fraction == "Particle"))
summary(lm_prod_vs_shannon_PA)##
## Call:
## lm(formula = frac_bacprod ~ mean, data = filter(shannon, fraction ==
## "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.9022 -2.9150 -0.5875 1.6713 12.0544
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -40.320 14.007 -2.878 0.01643 *
## mean 11.089 3.068 3.614 0.00473 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.693 on 10 degrees of freedom
## Multiple R-squared: 0.5664, Adjusted R-squared: 0.5231
## F-statistic: 13.06 on 1 and 10 DF, p-value: 0.004734# Cross validate particle-associated for a better estimate of the adjusted R-squared
cv_lm_prod_vs_shannon_PA <- train(
frac_bacprod ~ mean, data = filter(shannon, fraction == "Particle"),
method ='lm',
trControl = trainControl(method ="repeatedcv", number = 3, repeats = 100),
tuneGrid = expand.grid(intercept = TRUE))
cv_lm_prod_vs_shannon_PA$results # Particle-Associated CV results ## intercept RMSE Rsquared RMSESD RsquaredSD
## 1 TRUE 6.643704 0.7014407 2.470687 0.2662234summary(lm(frac_bacprod ~ mean, data = filter(shannon, fraction == "Free"))) # Free Living ##
## Call:
## lm(formula = frac_bacprod ~ mean, data = filter(shannon, fraction ==
## "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.004 -10.708 -3.738 6.632 37.129
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -13.37 73.87 -0.181 0.860
## mean 9.40 18.50 0.508 0.622
##
## Residual standard error: 18.15 on 10 degrees of freedom
## Multiple R-squared: 0.02516, Adjusted R-squared: -0.07233
## F-statistic: 0.2581 on 1 and 10 DF, p-value: 0.6225################### Inverse Simpson ###################
summary(lm(frac_bacprod ~ mean, data = invsimps)) # All samples together##
## Call:
## lm(formula = frac_bacprod ~ mean, data = invsimps)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.269 -10.298 -4.916 5.866 46.452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.1577 6.0225 2.019 0.0559 .
## mean 0.1629 0.1731 0.941 0.3570
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.25 on 22 degrees of freedom
## Multiple R-squared: 0.03868, Adjusted R-squared: -0.005019
## F-statistic: 0.8851 on 1 and 22 DF, p-value: 0.357# Particle-Associated samples
lm_prod_vs_invsimps_PA <- lm(frac_bacprod ~ mean, data = filter(invsimps, fraction == "Particle"))
summary(lm_prod_vs_invsimps_PA)##
## Call:
## lm(formula = frac_bacprod ~ mean, data = filter(invsimps, fraction ==
## "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.5589 -2.1093 -0.1969 0.8752 7.8282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.45199 2.43866 -0.185 0.85666
## mean 0.29344 0.05781 5.076 0.00048 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.571 on 10 degrees of freedom
## Multiple R-squared: 0.7204, Adjusted R-squared: 0.6925
## F-statistic: 25.77 on 1 and 10 DF, p-value: 0.0004804# Cross validate particle-associated for a better estimate of the adjusted R-squared
cv_lm_prod_vs_invsimps_PA <- train(
frac_bacprod ~ mean, data = filter(invsimps, fraction == "Particle"),
method ='lm',
trControl = trainControl(method ="repeatedcv", number = 3, repeats = 100),
tuneGrid = expand.grid(intercept = TRUE))
cv_lm_prod_vs_invsimps_PA$results # Cross Validated PA results## intercept RMSE Rsquared RMSESD RsquaredSD
## 1 TRUE 5.27195 0.7648654 2.096906 0.2523474#Free Living Samples
summary(lm(frac_bacprod ~ mean, data = filter(invsimps, fraction == "Free")))##
## Call:
## lm(formula = frac_bacprod ~ mean, data = filter(invsimps, fraction ==
## "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.765 -9.356 -4.445 6.116 36.017
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.0985 16.7052 0.664 0.521
## mean 0.5379 0.6598 0.815 0.434
##
## Residual standard error: 17.8 on 10 degrees of freedom
## Multiple R-squared: 0.06233, Adjusted R-squared: -0.03143
## F-statistic: 0.6648 on 1 and 10 DF, p-value: 0.4339################### Simpson's Evenness ###################
summary(lm(frac_bacprod ~ mean, data = simpseven)) # All samples together##
## Call:
## lm(formula = frac_bacprod ~ mean, data = simpseven)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.000 -7.163 -3.815 3.392 45.845
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8524 9.2286 -0.092 0.9272
## mean 274.1606 134.4253 2.040 0.0536 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.26 on 22 degrees of freedom
## Multiple R-squared: 0.159, Adjusted R-squared: 0.1208
## F-statistic: 4.16 on 1 and 22 DF, p-value: 0.05359# Particle-Associated
lm_prod_vs_simpseven_PA <- lm(frac_bacprod ~ mean, data = filter(simpseven, fraction == "Particle"))
summary(lm_prod_vs_simpseven_PA)##
## Call:
## lm(formula = frac_bacprod ~ mean, data = filter(simpseven, fraction ==
## "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.973 -2.229 -1.086 1.356 12.380
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.829 4.539 -0.844 0.41865
## mean 234.057 71.238 3.286 0.00821 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.995 on 10 degrees of freedom
## Multiple R-squared: 0.5191, Adjusted R-squared: 0.471
## F-statistic: 10.79 on 1 and 10 DF, p-value: 0.008211# Cross validate particle-associated for a better estimate of the adjusted R-squared
cv_lm_prod_vs_simpseven_PA <- train(
frac_bacprod ~ mean, data = filter(simpseven, fraction == "Particle"),
method ='lm',
trControl = trainControl(method ="repeatedcv", number = 3, repeats = 100),
tuneGrid = expand.grid(intercept = TRUE))
cv_lm_prod_vs_simpseven_PA$results # Particle-Associated CV results ## intercept RMSE Rsquared RMSESD RsquaredSD
## 1 TRUE 6.377862 0.6423519 2.906471 0.2954007summary(lm(frac_bacprod ~ mean, data = filter(simpseven, fraction == "Free"))) # Free Living ##
## Call:
## lm(formula = frac_bacprod ~ mean, data = filter(simpseven, fraction ==
## "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.389 -12.029 -3.306 4.668 39.946
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.85 23.52 0.674 0.516
## mean 114.96 321.02 0.358 0.728
##
## Residual standard error: 18.27 on 10 degrees of freedom
## Multiple R-squared: 0.01266, Adjusted R-squared: -0.08607
## F-statistic: 0.1282 on 1 and 10 DF, p-value: 0.7277#################################### Per-Cell Production ####################################
################### Richness ###################
summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = richness)) # All samples together ##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mean, data = richness)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8668 -0.2124 0.1045 0.2133 0.6473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.4591277 0.2212633 -38.231 < 2e-16 ***
## mean 0.0028988 0.0004641 6.246 3.4e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3804 on 21 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.6501, Adjusted R-squared: 0.6334
## F-statistic: 39.02 on 1 and 21 DF, p-value: 3.395e-06# Particle-Associated
lm_percell_prod_vs_rich_PA <- lm(log10(fracprod_per_cell_noinf) ~ mean, data = filter(richness, fraction == "Particle"))
summary(lm_percell_prod_vs_rich_PA)##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mean, data = filter(richness,
## fraction == "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.55347 -0.21545 -0.01066 0.12536 0.58830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.0617648 0.3717671 -21.685 4.44e-09 ***
## mean 0.0023794 0.0006348 3.748 0.00457 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3506 on 9 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.6095, Adjusted R-squared: 0.5662
## F-statistic: 14.05 on 1 and 9 DF, p-value: 0.004567# Cross validate particle-associated for a better estimate of the adjusted R-squared
cv_lm_percell_prod_vs_rich_PA <- train(
log10(fracprod_per_cell_noinf) ~ mean, data = filter(richness, fraction == "Particle" & norep_filter_name != "MOTEJ515"),
method ='lm',
trControl = trainControl(method ="repeatedcv", number = 3, repeats = 100),
tuneGrid = expand.grid(intercept = TRUE))
cv_lm_percell_prod_vs_rich_PA$results # Particle-Associated CV results ## intercept RMSE Rsquared RMSESD RsquaredSD
## 1 TRUE 0.4164107 0.6183409 0.150773 0.3257465summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = filter(richness, fraction == "Free"))) # Free Living ##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mean, data = filter(richness,
## fraction == "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.71719 -0.13833 0.07155 0.23581 0.56949
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.135758 0.471253 -17.264 8.99e-09 ***
## mean 0.001657 0.001353 1.225 0.249
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3859 on 10 degrees of freedom
## Multiple R-squared: 0.1304, Adjusted R-squared: 0.04344
## F-statistic: 1.499 on 1 and 10 DF, p-value: 0.2488################### Shannon Entropy ###################
summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = shannon)) # All samples together ##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mean, data = shannon)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.03514 -0.15042 -0.03394 0.26568 0.82794
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.9036 0.7534 -14.472 2.14e-12 ***
## mean 0.8805 0.1763 4.993 6.09e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4348 on 21 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.5428, Adjusted R-squared: 0.521
## F-statistic: 24.93 on 1 and 21 DF, p-value: 6.09e-05# Particle-Associated
lm_percell_prod_vs_shannon_PA <- lm(log10(fracprod_per_cell_noinf) ~ mean, data = filter(shannon, fraction == "Particle"))
summary(lm_percell_prod_vs_shannon_PA)##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mean, data = filter(shannon,
## fraction == "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.36686 -0.23571 -0.01330 0.03961 0.70312
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.8897 0.8820 -11.213 1.37e-06 ***
## mean 0.6993 0.1935 3.614 0.00562 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3584 on 9 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.5921, Adjusted R-squared: 0.5468
## F-statistic: 13.06 on 1 and 9 DF, p-value: 0.00562# Cross validate particle-associated for a better estimate of the adjusted R-squared
cv_lm_percell_prod_vs_shannon_PA <- train(
log10(fracprod_per_cell_noinf) ~ mean, data = filter(shannon, fraction == "Particle" & norep_filter_name != "MOTEJ515"),
method ='lm',
trControl = trainControl(method ="repeatedcv", number = 3, repeats = 100),
tuneGrid = expand.grid(intercept = TRUE))
cv_lm_percell_prod_vs_shannon_PA$results # Particle-Associated CV results ## intercept RMSE Rsquared RMSESD RsquaredSD
## 1 TRUE 0.4239945 0.6778371 0.1361161 0.3173377summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = filter(shannon, fraction == "Free"))) # Free Living ##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mean, data = filter(shannon,
## fraction == "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.72444 -0.17785 0.08114 0.13946 0.67366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.8666 1.6332 -5.429 0.000289 ***
## mean 0.3243 0.4091 0.793 0.446298
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4014 on 10 degrees of freedom
## Multiple R-squared: 0.05914, Adjusted R-squared: -0.03495
## F-statistic: 0.6285 on 1 and 10 DF, p-value: 0.4463################### Inverse Simpson ###################
summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = invsimps)) # All samples together ##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mean, data = invsimps)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.97302 -0.28199 -0.05285 0.32003 0.99088
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.86039 0.18153 -43.301 < 2e-16 ***
## mean 0.02355 0.00525 4.485 0.000204 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4596 on 21 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.4893, Adjusted R-squared: 0.4649
## F-statistic: 20.12 on 1 and 21 DF, p-value: 0.0002037# Particle-Associated
lm_percell_prod_vs_invsimps_PA <- lm(log10(fracprod_per_cell_noinf) ~ mean, data = filter(invsimps, fraction == "Particle"))
summary(lm_percell_prod_vs_invsimps_PA)##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mean, data = filter(invsimps,
## fraction == "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.28651 -0.18384 -0.11125 0.07337 0.56444
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.360961 0.159490 -46.153 5.27e-12 ***
## mean 0.018087 0.003759 4.812 0.000958 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2969 on 9 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.7201, Adjusted R-squared: 0.689
## F-statistic: 23.15 on 1 and 9 DF, p-value: 0.0009581# Cross validate particle-associated for a better estimate of the adjusted R-squared
cv_lm_percell_prod_vs_invsimps_PA <- train(
log10(fracprod_per_cell_noinf) ~ mean, data = filter(invsimps, fraction == "Particle" & norep_filter_name != "MOTEJ515"),
method ='lm',
trControl = trainControl(method ="repeatedcv", number = 3, repeats = 100),
tuneGrid = expand.grid(intercept = TRUE))
cv_lm_percell_prod_vs_invsimps_PA$results # Particle-Associated CV results ## intercept RMSE Rsquared RMSESD RsquaredSD
## 1 TRUE 0.350356 0.7200567 0.1138028 0.3340058summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = filter(invsimps, fraction == "Free"))) # Free Living ##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mean, data = filter(invsimps,
## fraction == "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.68269 -0.11512 0.01325 0.17742 0.63175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.03513 0.35685 -22.517 6.72e-10 ***
## mean 0.01910 0.01409 1.355 0.205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3803 on 10 degrees of freedom
## Multiple R-squared: 0.1551, Adjusted R-squared: 0.07064
## F-statistic: 1.836 on 1 and 10 DF, p-value: 0.2052################### Simpson's Evenness ###################
summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = simpseven)) # All samples together ##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mean, data = simpseven)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.06874 -0.41920 0.04553 0.34511 1.56565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.4496 0.4116 -18.10 2.74e-14 ***
## mean 4.3470 6.0344 0.72 0.479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6353 on 21 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02411, Adjusted R-squared: -0.02236
## F-statistic: 0.5189 on 1 and 21 DF, p-value: 0.4792# Particle-Associated
lm_percell_prod_vs_simpseven_PA <- lm(log10(fracprod_per_cell_noinf) ~ mean, data = filter(simpseven, fraction == "Particle"))
summary(lm_percell_prod_vs_simpseven_PA)##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mean, data = filter(simpseven,
## fraction == "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4726 -0.2230 -0.1106 0.1248 0.6887
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.5941 0.2885 -26.324 7.96e-10 ***
## mean 15.1887 4.6341 3.278 0.00957 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3788 on 9 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.5441, Adjusted R-squared: 0.4935
## F-statistic: 10.74 on 1 and 9 DF, p-value: 0.009566# Cross validate particle-associated for a better estimate of the adjusted R-squared
cv_lm_percell_prod_vs_simpseven_PA <- train(
log10(fracprod_per_cell_noinf) ~ mean, data = filter(simpseven, fraction == "Particle" & norep_filter_name != "MOTEJ515"),
method ='lm',
trControl = trainControl(method ="repeatedcv", number = 3, repeats = 100),
tuneGrid = expand.grid(intercept = TRUE))
cv_lm_percell_prod_vs_simpseven_PA$results # Particle-Associated CV results ## intercept RMSE Rsquared RMSESD RsquaredSD
## 1 TRUE 0.4354828 0.6660292 0.1432305 0.316585summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = filter(simpseven, fraction == "Free"))) # Free Living ##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mean, data = filter(simpseven,
## fraction == "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.63112 -0.24216 0.01388 0.14672 0.77426
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.9274 0.5202 -15.240 3e-08 ***
## mean 4.9361 7.1008 0.695 0.503
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4041 on 10 degrees of freedom
## Multiple R-squared: 0.04609, Adjusted R-squared: -0.0493
## F-statistic: 0.4832 on 1 and 10 DF, p-value: 0.5028R2 table
########## PUT A TABLE TOGETHER
# Per Liter Production
perliter_row1 <- c("Richness", "Per-Liter",
round(summary(lm_prod_vs_rich_PA)$adj.r.squared, digits = 3),
round(cv_lm_prod_vs_rich_PA$results$Rsquared, digits = 3),
round(cv_lm_prod_vs_rich_PA$results$RsquaredSD, digits = 3))
perliter_row2 <- c("Shannon_Entropy", "Per-Liter",
round(summary(lm_prod_vs_shannon_PA)$adj.r.squared, digits = 3),
round(cv_lm_prod_vs_shannon_PA$results$Rsquared, digits = 3),
round(cv_lm_prod_vs_shannon_PA$results$RsquaredSD, digits = 3))
perliter_row3 <- c("Inverse_Simpson", "Per-Liter",
round(summary(lm_prod_vs_invsimps_PA)$adj.r.squared, digits = 3),
round(cv_lm_prod_vs_invsimps_PA$results$Rsquared, digits = 3),
round(cv_lm_prod_vs_invsimps_PA$results$RsquaredSD, digits = 3))
perliter_row4 <- c("Simpsons_Evenness","Per-Liter",
round(summary(lm_prod_vs_simpseven_PA)$adj.r.squared, digits = 3),
round(cv_lm_prod_vs_simpseven_PA$results$Rsquared, digits = 3),
round(cv_lm_prod_vs_simpseven_PA$results$RsquaredSD, digits = 3))
# Per cell production
percell_row1 <- c("Richness", "Per-Cell",
round(summary(lm_percell_prod_vs_rich_PA)$adj.r.squared, digits = 3),
round(cv_lm_percell_prod_vs_rich_PA$results$Rsquared, digits = 3),
round(cv_lm_percell_prod_vs_rich_PA$results$RsquaredSD, digits = 3))
percell_row2 <- c("Shannon_Entropy", "Per-Cell",
round(summary(lm_percell_prod_vs_shannon_PA)$adj.r.squared, digits = 3),
round(cv_lm_percell_prod_vs_shannon_PA$results$Rsquared, digits = 3),
round(cv_lm_percell_prod_vs_shannon_PA$results$RsquaredSD, digits = 3))
percell_row3 <- c("Inverse_Simpson", "Per-Cell",
round(summary(lm_percell_prod_vs_invsimps_PA)$adj.r.squared, digits = 3),
round(cv_lm_percell_prod_vs_invsimps_PA$results$Rsquared, digits = 3),
round(cv_lm_percell_prod_vs_invsimps_PA$results$RsquaredSD, digits = 3))
percell_row4 <- c("Simpsons_Evenness", "Per-Cell",
round(summary(lm_percell_prod_vs_simpseven_PA)$adj.r.squared, digits = 3),
round(cv_lm_percell_prod_vs_simpseven_PA$results$Rsquared, digits = 3),
round(cv_lm_percell_prod_vs_simpseven_PA$results$RsquaredSD, digits = 3))
r2_table <- as.data.frame(rbind(perliter_row1, perliter_row2, perliter_row3, perliter_row4,
percell_row1, percell_row2, percell_row3, percell_row4))
colnames(r2_table) <- c("Diversity_Metric", "Production_Measure","Adjusted_R2","CV_R2", "CV_R2_SD")
row.names(r2_table) = NULL
pander(r2_table,
caption = "Supplemental Table 2: \n R2 estimates for heterotrophic production vs particle-associated diversity linear regressions.")| Diversity_Metric | Production_Measure | Adjusted_R2 | CV_R2 | CV_R2_SD |
|---|---|---|---|---|
| Richness | Per-Liter | 0.559 | 0.654 | 0.295 |
| Shannon_Entropy | Per-Liter | 0.523 | 0.701 | 0.266 |
| Inverse_Simpson | Per-Liter | 0.692 | 0.765 | 0.252 |
| Simpsons_Evenness | Per-Liter | 0.471 | 0.642 | 0.295 |
| Richness | Per-Cell | 0.566 | 0.618 | 0.326 |
| Shannon_Entropy | Per-Cell | 0.547 | 0.678 | 0.317 |
| Inverse_Simpson | Per-Cell | 0.689 | 0.72 | 0.334 |
| Simpsons_Evenness | Per-Cell | 0.493 | 0.666 | 0.317 |
Prepare Figure 2
######################################################### OBSERVED RICHNESS
rich_fraction_wilcox <- wilcox.test(mean ~ fraction, data = richness)
rich_fraction_wilcox##
## Wilcoxon rank sum test
##
## data: mean by fraction
## W = 129, p-value = 0.0004955
## alternative hypothesis: true location shift is not equal to 0filter(richness) %>%
group_by(fraction) %>%
summarize(mean(mean), sd(mean))## # A tibble: 2 x 3
## fraction `mean(mean)` `sd(mean)`
## <fctr> <dbl> <dbl>
## 1 Particle 556.7992 167.31404
## 2 Free 338.4242 85.98665# Make a data frame to draw significance line in boxplot (visually calculated)
rich_nums <- data.frame(a = c(1.15,1.15,1.85,1.85), b = c(920, 930, 930, 920)) # WholePart vs WholeFree
rich_distribution_plot <-
ggplot(richness, aes(y = mean, x = fraction)) +
scale_fill_manual(values = fraction_colors) +
geom_jitter(size = 3, aes(fill = fraction, shape = season), width = 0.2) +
geom_boxplot(alpha = 0.5, outlier.shape = NA, aes(fill = fraction)) +
scale_y_continuous(limits = c(150,950), breaks = seq(from = 0, to =925, by = 150)) +
xlab("Observed Richness") + xlab("Fraction") +
scale_shape_manual(values = season_shapes) +
geom_path(data = rich_nums, aes(x = a, y = b), linetype = 1, color = "#424645") +
annotate("text", x=1.5, y=790, fontface = "bold", size = 4, color = "#424645",
label= paste("p =", round(rich_fraction_wilcox$p.value, digits = 5))) +
theme(legend.position = "none",# axis.title.y = element_blank(),
axis.title.x = element_blank(), axis.text.x = element_blank()) +
coord_flip()
# Richness vs Per-Liter Heterotrophic Production Plot
prod_vs_rich_plot <-
ggplot(richness, aes(x=mean, y=frac_bacprod)) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_errorbar(aes(ymin = frac_bacprod - SD_frac_bacprod, ymax = frac_bacprod + SD_frac_bacprod, color = fraction)) + # Y-axis errorbars
geom_point(size = 3.5, color = "black", aes(fill = fraction, shape = season)) +
scale_color_manual(values = fraction_colors) +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
ylab("\n Community Production \n (μgC/L/day)") +
xlab("Observed Richness") +
geom_smooth(data=filter(richness, fraction == "Particle"), method='lm', color = "#FF6600", fill = "#FF6600") +
scale_x_continuous(limits = c(150,950), breaks = seq(from = 0, to =925, by = 150)) +
annotate("text", x = 790, y=45, color = "#FF6600", fontface = "bold", size = 4,
label = paste("R2 =", round(summary(lm_prod_vs_rich_PA)$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm_prod_vs_rich_PA)$coefficients[,4][2]), digits = 3))) +
theme(legend.position = "none", axis.title.x = element_blank(), axis.text.x = element_blank())
# Richness vs Per-cell herterotrophic production Plot
percell_prod_vs_rich_plot <-
ggplot(richness, aes(x=mean, y=log10(fracprod_per_cell_noinf))) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_point(size = 3.5, color = "black", aes(fill = fraction, shape = season)) +
scale_color_manual(values = fraction_colors) +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
ylab("\n log10(Per-Capita Production)\n (μgC/cell/day)") +
xlab("Observed Richness") +
geom_smooth(data=filter(richness, fraction == "Particle"), method='lm', color = "#FF6600", fill = "#FF6600") +
scale_x_continuous(limits = c(150,950), breaks = seq(from = 0, to =925, by = 150)) +
#scale_y_continuous(limits = c(-8e-7,5e-6), breaks = seq(from = 0, to = 6e-7, by = 3e-7)) +
annotate("text", x = 790, y=-7.5, color = "#FF6600", fontface = "bold", size = 4,
label = paste("R2 =", round(summary(lm_percell_prod_vs_rich_PA)$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm_percell_prod_vs_rich_PA)$coefficients[,4][2]), digits = 3))) +
#annotate("text", x = 250, y=55, color = "skyblue", fontface = "bold", label = paste("Free = NS")) +
theme(legend.title = element_blank(), legend.position ="bottom",
legend.text = element_text(size = 14))
######################################################### INVERSE SIMPSON
invsimps_fraction_wilcox <- wilcox.test(mean ~ fraction, data = invsimps)
invsimps_fraction_wilcox##
## Wilcoxon rank sum test
##
## data: mean by fraction
## W = 81, p-value = 0.6297
## alternative hypothesis: true location shift is not equal to 0filter(invsimps) %>%
group_by(fraction) %>%
summarize(mean(mean), sd(mean))## # A tibble: 2 x 3
## fraction `mean(mean)` `sd(mean)`
## <fctr> <dbl> <dbl>
## 1 Particle 35.47659 23.843325
## 2 Free 24.09219 8.136901# Make a data frame to draw significance line in boxplot (visually calculated)
invsimps_nums <- data.frame(a = c(1.15,1.15,1.85,1.85), b = c(83,85,85,83)) # WholePart vs WholeFree
invsimps_distribution_plot <-
ggplot(invsimps, aes(y = mean, x = fraction)) +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
geom_jitter(size = 3, aes(fill = fraction, shape = season), width = 0.2) +
geom_boxplot(alpha = 0.5, outlier.shape = NA, aes(fill = fraction)) +
scale_y_continuous(limits = c(0,85), breaks = seq(from = 0, to = 85, by = 20)) +
ylab("Inverse Simpson") + xlab("Fraction") +
geom_path(data = invsimps_nums, aes(x = a, y = b), linetype = 1, color = "#424645") +
annotate("text", x=1.5, y=80, fontface = "bold", size = 4, color = "#424645", label= "NS") +
theme(legend.position = "none", #axis.title.y = element_blank(),
axis.title.x = element_blank(), axis.text.x = element_blank()) +
coord_flip()
# INVERSE SIMPSON
# Plot Inverse Simpson vs Per-Liter Heterotrophic Production
prod_vs_invsimps_plot <-
ggplot(invsimps, aes(x=mean, y=frac_bacprod)) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_errorbar(aes(ymin = frac_bacprod - SD_frac_bacprod, ymax = frac_bacprod + SD_frac_bacprod, color = fraction)) + # Y-axis errorbars
geom_point(size = 3.5, color = "black", aes(fill = fraction, shape = season)) +
scale_color_manual(values = fraction_colors) +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
ylab("Heterotrophic Production \n (μgC/L/day)") +
xlab("Inverse Simpson") +
geom_smooth(data=filter(invsimps, fraction == "Particle"), method='lm', color = "#FF6600", fill = "#FF6600") +
scale_x_continuous(limits = c(0,85), breaks = seq(from = 0, to = 85, by = 20)) +
annotate("text", x = 70, y=45, color = "#FF6600", fontface = "bold",size = 4,
label = paste("R2 =", round(summary(lm_prod_vs_invsimps_PA)$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm_prod_vs_invsimps_PA)$coefficients[,4][2]), digits = 4))) +
theme(legend.position = "none", axis.title.x = element_blank(), axis.text.x = element_blank())
# Inverse Simpson vs per-cell production Plot
percell_prod_vs_invsimps_plot <-
ggplot(invsimps, aes(x=mean, y=log10(fracprod_per_cell_noinf))) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_point(size = 3.5, color = "black", aes(fill = fraction, shape = season)) +
scale_color_manual(values = fraction_colors) +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
ylab("log10(production/cell)\n (μgC/cell/day)") +
xlab("Inverse Simpson") +
geom_smooth(data=filter(invsimps, fraction == "Particle"), method='lm', color = "#FF6600", fill = "#FF6600") +
scale_x_continuous(limits = c(0,85), breaks = seq(from = 0, to = 85, by = 20)) +
#scale_y_continuous(limits = c(-8e-7,5e-6), breaks = seq(from = 0, to = 6e-7, by = 3e-7)) +
annotate("text", x = 70, y=-7.5, color = "#FF6600", fontface = "bold",size = 4,
label = paste("R2 =", round(summary(lm_percell_prod_vs_invsimps_PA)$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm_percell_prod_vs_invsimps_PA)$coefficients[,4][2]), digits = 4))) +
theme(legend.title = element_blank(), legend.position ="bottom",
legend.text = element_text(size = 14))
# Plot Inverse simpson plots altogether
invsimps_plots <- plot_grid(invsimps_distribution_plot, prod_vs_invsimps_plot, percell_prod_vs_invsimps_plot,
labels = c("B", "D", "F"), ncol = 1, nrow = 3,
rel_heights = c(0.5, 0.8, 1.1),
align = "v")Plot Figure 2
# All 3 richness plots together
rich_plots <- plot_grid(rich_distribution_plot, prod_vs_rich_plot,
percell_prod_vs_rich_plot + theme(legend.position = "none"),
labels = c("A", "C", "E"), ncol = 1, nrow = 3,
rel_heights = c(0.5, 0.8, 1),
align = "v")
invsimps_plots_noyaxis <-
plot_grid(invsimps_distribution_plot + theme(axis.title.y = element_blank(), axis.text.y = element_blank()),
prod_vs_invsimps_plot + theme(axis.title.y = element_blank()),
percell_prod_vs_invsimps_plot + theme(axis.title.y = element_blank(), legend.position = "none"),
labels = c("B", "D", "F"), ncol = 1, nrow = 3,
rel_heights = c(0.5, 0.8, 1),
align = "v")
figure2_row1 <- plot_grid(rich_plots, invsimps_plots_noyaxis,
ncol = 2, nrow = 1, rel_widths = c(1, 0.75),
align = "h")
######## FIGURE 2
plot_grid(figure2_row1, season_legend,
ncol = 1, nrow = 2,
rel_heights = c(1, 0.05))
Prepare Figure S2
######################################################### SHANNON_ENTROPY
shannon_fraction_wilcox <- wilcox.test(mean ~ fraction, data = shannon)
shannon_fraction_wilcox##
## Wilcoxon rank sum test
##
## data: mean by fraction
## W = 119, p-value = 0.00556
## alternative hypothesis: true location shift is not equal to 0filter(shannon) %>%
group_by(fraction) %>%
summarize(mean(mean), sd(mean))## # A tibble: 2 x 3
## fraction `mean(mean)` `sd(mean)`
## <fctr> <dbl> <dbl>
## 1 Particle 4.534078 0.5594638
## 2 Free 3.982314 0.2958221# Make a data frame to draw significance line in boxplot (visually calculated)
shannon_nums <- data.frame(a = c(1.15,1.15,1.85,1.85), b = c(5.8, 5.9, 5.9, 5.8)) # WholePart vs WholeFree
shannon_distribution_plot <-
ggplot(shannon, aes(y = mean, x = fraction)) +
scale_fill_manual(values = fraction_colors) +
geom_jitter(size = 3, aes(shape = season, fill = fraction), width = 0.2) +
geom_boxplot(alpha = 0.5, outlier.shape = NA, aes(fill = fraction)) +
scale_y_continuous(limits = c(3.5,6), breaks = seq(from = 3, to =6, by = 0.5)) +
xlab("Shannon Entropy") +
xlab("Fraction") +
scale_shape_manual(values = season_shapes) +
# Add line and pval
geom_path(data = shannon_nums, aes(x = a, y = b), linetype = 1, color = "#424645") +
annotate("text", x=1.5, y=5.6, fontface = "bold", size = 4, color = "#424645",
label= paste("*p =", round(shannon_fraction_wilcox$p.value, digits = 3))) +
theme(legend.position = "none",# axis.title.y = element_blank(),
axis.title.x = element_blank(), axis.text.x = element_blank()) +
coord_flip()
# Shannon vs Per-Liter Heterotrophic Production Plot
prod_vs_shannon_plot <-
ggplot(shannon, aes(x=mean, y=frac_bacprod)) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_errorbar(aes(ymin = frac_bacprod - SD_frac_bacprod, ymax = frac_bacprod + SD_frac_bacprod, color = fraction)) + # Y-axis errorbars
geom_point(size = 3.5, color = "black", aes(fill = fraction, shape = season)) +
scale_color_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
scale_fill_manual(values = fraction_colors) +
ylab("Heterotrophic Production \n (μgC/L/day)") +
scale_x_continuous(limits = c(3.5,6), breaks = seq(from = 3, to =6, by = 0.5)) +
xlab("Shannon Entropy") +
geom_smooth(data=filter(shannon, fraction == "Particle"), method='lm', color = "#FF6600", fill = "#FF6600") +
annotate("text", x = 5.25, y=45, color = "#FF6600", fontface = "bold", size = 4,
label = paste("R2 =", round(summary(lm_prod_vs_shannon_PA)$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm_prod_vs_shannon_PA)$coefficients[,4][2]), digits = 3))) +
theme(legend.position = "none", axis.title.x = element_blank(), axis.text.x = element_blank())
# Richness vs Per-cell herterotrophic production Plot
percell_prod_vs_shannon_plot <-
ggplot(shannon, aes(x=mean, y=log10(fracprod_per_cell_noinf))) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_point(size = 3.5, color = "black", aes(fill = fraction, shape = season)) +
scale_color_manual(values = fraction_colors) +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
ylab("log10(production/cell)\n (μgC/cell/day)") +
xlab("Shannon Entropy") +
geom_smooth(data=filter(shannon, fraction == "Particle"), method='lm', color = "#FF6600", fill = "#FF6600") +
scale_x_continuous(limits = c(3.5,6), breaks = seq(from = 3, to =6, by = 0.5)) +
#scale_y_continuous(limits = c(-8e-7,5e-6), breaks = seq(from = 0, to = 6e-7, by = 3e-7)) +
annotate("text", x = 5.25, y=-7.5, color = "#FF6600", fontface = "bold", size = 4,
label = paste("R2 =", round(summary(lm_percell_prod_vs_shannon_PA)$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm_percell_prod_vs_shannon_PA)$coefficients[,4][2]), digits = 3))) +
#annotate("text", x = 250, y=55, color = "skyblue", fontface = "bold", label = paste("Free = NS")) +
theme(legend.title = element_blank(), legend.position = "none")
shannon_plots <- plot_grid(shannon_distribution_plot, prod_vs_shannon_plot, percell_prod_vs_shannon_plot,
labels = c("A", "C", "E"), ncol = 1, nrow = 3,
rel_heights = c(0.5, 0.8, 1.1),
align = "v")
######################################################### INVERSE SIMPSON
simpseven_fraction_wilcox <- wilcox.test(mean ~ fraction, data = simpseven)
simpseven_fraction_wilcox##
## Wilcoxon rank sum test
##
## data: mean by fraction
## W = 55, p-value = 0.3474
## alternative hypothesis: true location shift is not equal to 0filter(simpseven) %>%
group_by(fraction) %>%
summarize(mean(mean), sd(mean))## # A tibble: 2 x 3
## fraction `mean(mean)` `sd(mean)`
## <fctr> <dbl> <dbl>
## 1 Particle 0.05890559 0.02537484
## 2 Free 0.07138806 0.01716014# Make a data frame to draw significance line in boxplot (visually calculated)
simpseven_nums <- data.frame(a = c(1.15,1.15,1.85,1.85), b = c(0.14, 0.15, 0.15, 0.14)) # WholePart vs WholeFree
simpseven_distribution_plot <-
ggplot(simpseven, aes(y = mean, x = fraction)) +
scale_fill_manual(values = fraction_colors) +
geom_jitter(size = 3, aes(fill = fraction, shape = season), width = 0.2) +
geom_boxplot(alpha = 0.5, outlier.shape = NA, aes(fill = fraction)) +
scale_y_continuous(limits = c(0,0.15), breaks = seq(from = 0, to = 0.15, by = 0.03)) +
ylab("Simpson's Evenness") +
scale_shape_manual(values = season_shapes) +
xlab("Fraction") +
geom_path(data = simpseven_nums, aes(x = a, y = b), linetype = 1, color = "#424645") +
annotate("text", x=1.5, y=0.14, fontface = "bold", size = 4, color = "#424645", label= "NS") +
theme(legend.position = "none",
axis.title.x = element_blank(), axis.text.x = element_blank()) +
coord_flip()
# SIMPSONS EVENNESS
# Plot Inverse Simpson vs Per-Liter Heterotrophic Production
prod_vs_simpseven_plot <-
ggplot(simpseven, aes(x=mean, y=frac_bacprod)) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_errorbar(aes(ymin = frac_bacprod - SD_frac_bacprod, ymax = frac_bacprod + SD_frac_bacprod, color = fraction)) + # Y-axis errorbars
geom_point(size = 3.5, color = "black", aes(fill = fraction, shape = season)) +
scale_color_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
scale_fill_manual(values = fraction_colors) +
ylab("Heterotrophic Production \n (μgC/L/day)") +
ylab("Simpson's Evenness") +
geom_smooth(data=filter(simpseven, fraction == "Particle"), method='lm', color = "#FF6600", fill = "#FF6600") +
scale_x_continuous(limits = c(0,0.15), breaks = seq(from = 0, to = 0.15, by = 0.03)) +
annotate("text", x = 0.14, y=15, color = "#FF6600", fontface = "bold",size = 4,
label = paste("R2 =", round(summary(lm_prod_vs_simpseven_PA)$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm_prod_vs_simpseven_PA)$coefficients[,4][2]), digits = 3))) +
theme(legend.position = "none", axis.title.x = element_blank(), axis.text.x = element_blank())
# Plot
percell_prod_vs_simpseven_plot <-
ggplot(simpseven, aes(x=mean, y=log10(fracprod_per_cell_noinf))) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_point(size = 3.5, color = "black", aes(fill = fraction, shape = season)) +
scale_color_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
scale_fill_manual(values = fraction_colors) +
ylab("log10(production/cell)\n (μgC/cell/day)") +
xlab("Simpson's Evenness") +
geom_smooth(data=filter(simpseven, fraction == "Particle"), method='lm', color = "#FF6600", fill = "#FF6600") +
scale_x_continuous(limits = c(0,0.15), breaks = seq(from = 0, to = 0.15, by = 0.03)) +
annotate("text", x = 0.14, y=-6.3, color = "#FF6600", fontface = "bold",size = 4,
label = paste("R2 =", round(summary(lm_percell_prod_vs_simpseven_PA)$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm_percell_prod_vs_simpseven_PA)$coefficients[,4][2]), digits = 4))) +
theme(legend.title = element_blank(), legend.position ="none")
simpseven_plots <- plot_grid(simpseven_distribution_plot, prod_vs_simpseven_plot, percell_prod_vs_simpseven_plot,
labels = c("B", "D", "F"), ncol = 1, nrow = 3,
rel_heights = c(0.5, 0.8, 1.1),
align = "v")R2_val <- grobTree(textGrob(bquote(Adj~R^2 == .(paste(round(summary(lm_prod_vs_shannon_PA)$adj.r.squared, digits = 2)))),
x=0.5, y=0.5, gp=gpar(col="#FF6600", size=12, fontface="bold")))## Error in grobTree(textGrob(bquote(Adj ~ R^2 == .(paste(round(summary(lm_prod_vs_shannon_PA)$adj.r.squared, : could not find function "grobTree"R2 <- grobTree(textGrob(bquote(Adj~R^2~ . (paste("=", round(summary(lm_prod_vs_shannon_PA)$adj.r.squared, digits = 2)))),
#paste("p =", round(unname(summary(lm_prod_vs_shannon_PA)$coefficients[,4][2]), digits = 3)),
x=0.5, y=0.75, gp=gpar(col="#FF6600", size=12, fontface="bold")))## Error in grobTree(textGrob(bquote(Adj ~ R^2 ~ .(paste("=", round(summary(lm_prod_vs_shannon_PA)$adj.r.squared, : could not find function "grobTree"prod_vs_shannon_plot +
xlab("Shannon Entropy") + annotation_custom(R2)## Error: object of type 'closure' is not subsettable
Plot Figure S2
simpseven_plots_noyaxis <-
plot_grid(simpseven_distribution_plot + theme(axis.title.y = element_blank(), axis.text.y = element_blank()),
prod_vs_simpseven_plot + theme(axis.title.y = element_blank()),
percell_prod_vs_simpseven_plot + theme(axis.title.y = element_blank()),
labels = c("B", "D", "F"), ncol = 1, nrow = 3,
rel_heights = c(0.5, 0.8, 1.1),
align = "v")
figS2_row1 <- plot_grid(shannon_plots, simpseven_plots_noyaxis,
ncol = 2, nrow = 1, rel_widths = c(1, 0.825),
align = "h")
plot_grid(figS2_row1, season_legend,
ncol = 1, nrow = 2, rel_heights = c(1, 0.05))
lm_simpseven_bulkprod <- lm(frac_bacprod ~ mean,
data = filter(otu_alphadiv, measure == "Simpsons_Evenness"))
ggplot(filter(otu_alphadiv, measure == "Simpsons_Evenness"),
aes(x = mean, y = frac_bacprod)) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_errorbar(aes(ymin = frac_bacprod - SD_frac_bacprod, ymax = frac_bacprod + SD_frac_bacprod, color = fraction)) + # Y-axis errorbars
ylab("Heterotrophic Production \n(ug C/L/day)") +
xlab("Simpson's Evenness") +
geom_point(size = 3, shape = 21, aes(fill = fraction)) +
geom_smooth(method = "lm", color = "#424645", fill = "#424645", alpha = 0.3) +
scale_fill_manual(values = fraction_colors) +
scale_color_manual(values = fraction_colors) +
theme(legend.position = c(0.15, 0.9), legend.title = element_blank()) +
annotate("text", x = 0.115, y=55, color = "#424645", fontface = "bold", size = 4,
label = paste("R2 =", round(summary(lm_simpseven_bulkprod)$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm_simpseven_bulkprod)$coefficients[,4][2]), digits = 3))) 
Prepare Figure S3
plot_all_rich_percell <-
ggplot(richness, aes(x=mean, y=log10(fracprod_per_cell_noinf))) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd), color = "grey", alpha = 0.7) + # X-axis errorbars
geom_point(size = 3.5, aes(shape = season), color = "black", fill = "grey") +
scale_shape_manual(values = season_shapes) +
ylab("log10(production/cell)\n (μgC/cell/day)") + xlab("Observed Richness") +
geom_smooth(method='lm', color = "#424645", fill = "#424645") +
scale_x_continuous(limits = c(150,950), breaks = seq(from = 0, to =925, by = 150)) +
scale_y_continuous(limits = c(-8.5, -5.4), breaks = seq(from = -9, to = -5, by = 1)) +
annotate("text", x = 790, y=-8, color = "#424645", fontface = "bold", size = 4,
label = paste("R2 =", round(summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = richness))$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = richness))$coefficients[,4][2]), digits = 6))) +
theme(legend.title = element_blank(), legend.position ="bottom",
legend.text = element_text(size = 14))
plot_all_shannon_percell <-
ggplot(shannon, aes(x=mean, y=log10(fracprod_per_cell_noinf))) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd), color = "grey", alpha = 0.7) + # X-axis errorbars
geom_point(size = 3.5, aes(shape = season), color = "black", fill = "grey") +
scale_shape_manual(values = season_shapes) +
ylab("log10(production/cell)\n (μgC/cell/day)") + xlab("Shannon Entropy") +
geom_smooth(method='lm', color = "#424645", fill = "#424645") +
scale_x_continuous(limits = c(3.5,6), breaks = seq(from = 3, to =6, by = 0.5)) +
scale_y_continuous(limits = c(-8.5, -5.4), breaks = seq(from = -9, to = -5, by = 1)) +
annotate("text", x = 5.25, y=-8, color = "#424645", fontface = "bold", size = 4,
label = paste("R2 =", round(summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = shannon))$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = shannon))$coefficients[,4][2]), digits = 5))) +
theme(legend.title = element_blank(), legend.position ="bottom",
legend.text = element_text(size = 14))
plot_all_invsimps_percell <-
ggplot(invsimps, aes(x=mean, y=log10(fracprod_per_cell_noinf))) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd), color = "grey", alpha = 0.7) + # X-axis errorbars
geom_point(size = 3.5, aes(shape = season), color = "black", fill = "grey") +
scale_shape_manual(values = season_shapes) +
ylab("log10(production/cell)\n (μgC/cell/day)") + xlab("Inverse Simpson") +
geom_smooth(method='lm', color = "#424645", fill = "#424645") +
scale_x_continuous(limits = c(0,85), breaks = seq(from = 0, to = 85, by = 20)) +
scale_y_continuous(limits = c(-8.5, -5.4), breaks = seq(from = -9, to = -5, by = 1)) +
annotate("text", x = 70, y=-8, color = "#424645", fontface = "bold", size = 4,
label = paste("R2 =", round(summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = invsimps))$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = invsimps))$coefficients[,4][2]), digits = 5))) +
theme(legend.title = element_blank(), legend.position ="bottom",
legend.text = element_text(size = 14))
plot_all_simpseven_percell <-
ggplot(simpseven, aes(x=mean, y=log10(fracprod_per_cell_noinf))) +
geom_errorbarh(aes(xmin = mean - sd, xmax = mean + sd), color = "grey", alpha = 0.7) + # X-axis errorbars
geom_point(size = 3.5, aes(shape = season), color = "black", fill = "grey") +
scale_shape_manual(values = season_shapes) +
ylab("log10(production/cell)\n (μgC/cell/day)") + xlab("Simpson's Evenness") +
geom_smooth(method='lm', color = "#424645", fill = "#424645") +
scale_x_continuous(limits = c(0,0.15), breaks = seq(from = 0, to = 0.15, by = 0.03)) +
scale_y_continuous(limits = c(-8.5, -5.4), breaks = seq(from = -9, to = -5, by = 1)) +
annotate("text", x = 0.12, y=-8, color = "#424645", fontface = "bold", size = 4,
label = paste("R2 =", round(summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = simpseven))$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm(log10(fracprod_per_cell_noinf) ~ mean, data = simpseven))$coefficients[,4][2]), digits = 2))) +
theme(legend.title = element_blank(), legend.position ="bottom",
legend.text = element_text(size = 14))
figs3_row1 <- plot_grid(plot_all_rich_percell + theme(legend.position = "none"),
plot_all_shannon_percell + theme(axis.title.y = element_blank(), legend.position = "none"),
plot_all_invsimps_percell + theme(axis.title.y = element_blank(), legend.position = "none"),
plot_all_simpseven_percell + theme(axis.title.y = element_blank(), legend.position = "none"),
align = "h", labels = c("A", "B", "C", "D"),
rel_widths = c(1.05, 0.9, 0.9, 0.9),
nrow = 1, ncol = 4)
######## FIGURE S3
# legend
legend_grey <- get_legend(plot_all_rich_percell)
plot_grid(figs3_row1, legend_grey,
ncol = 1, nrow = 2,
rel_heights = c(1, 0.05))
Calculate the Phylogenetic Diversity
# Read in the tree
RAREFIED_rm2_fasttree <- read.tree(file = "data/PhyloTree/newick_tree_rm2_rmN.tre")
# Load in data that has doubletons removed
load("data/PhyloTree/surface_PAFL_otu_pruned_RAREFIED_rm2.RData")
surface_PAFL_otu_pruned_RAREFIED_rm2## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1891 taxa and 24 samples ]
## sample_data() Sample Data: [ 24 samples by 70 sample variables ]
## tax_table() Taxonomy Table: [ 1891 taxa by 8 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1891 tips and 1889 internal nodes ]# Create the OTU table for picante
surface_PAFL_RAREFIED_rm2_otu <- matrix(otu_table(surface_PAFL_otu_pruned_RAREFIED_rm2), nrow = nrow(otu_table(surface_PAFL_otu_pruned_RAREFIED_rm2)))
rownames(surface_PAFL_RAREFIED_rm2_otu) <- sample_names(surface_PAFL_otu_pruned_RAREFIED_rm2)
colnames(surface_PAFL_RAREFIED_rm2_otu) <- taxa_names(surface_PAFL_otu_pruned_RAREFIED_rm2)
## Calculate input for SES_MPD
# Convert the abundance data to standardized abundanced vegan function `decostand' , NOTE: method = "total"
otu_decostand_total <- decostand(surface_PAFL_RAREFIED_rm2_otu, method = "total")
# check total abundance in each sample
apply(otu_decostand_total, 1, sum)## MBREJ515 MBREJ715 MBREJ915 MBREK515 MBREK715 MBREK915 MDPEJ515 MDPEJ715 MDPEJ915 MDPEK515 MDPEK715 MDPEK915 MINEJ515 MINEJ715 MINEJ915 MINEK515 MINEK715 MINEK915 MOTEJ515 MOTEJ715 MOTEJ915 MOTEK515 MOTEK715 MOTEK915
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1# check for mismatches/missing species between community data and phylo tree
RAREFIED_rm2_matches <- match.phylo.comm(RAREFIED_rm2_fasttree, otu_decostand_total)
# the resulting object is a list with $phy and $comm elements. replace our
# original data with the sorted/matched data
phy_RAREFIED_rm2 <- RAREFIED_rm2_matches$phy
comm_RAREFIED_rm2 <- RAREFIED_rm2_matches$comm
# Calculate the phylogenetic distances
phy_dist_RAREFIED_rm2 <- cophenetic(phy_RAREFIED_rm2)
## Calculate SES_MPD
###################################### INDEPENDENT SWAP ############################################
# calculate standardized effect size mean pairwise distance (ses.mpd)
unweighted_sesMPD_indepswap_RAREFIED_rm2 <- ses.mpd(comm_RAREFIED_rm2, phy_dist_RAREFIED_rm2, null.model = "independentswap",
abundance.weighted = FALSE, runs = 999)
WEIGHTED_sesMPD_indepswap_RAREFIED_rm2 <- ses.mpd(comm_RAREFIED_rm2, phy_dist_RAREFIED_rm2, null.model = "independentswap",
abundance.weighted = TRUE, runs = 999)
# Gather div info
rich_df <- filter(otu_alphadiv, measure == "Richness") %>%
dplyr::select(norep_filter_name, mean, sd, frac_bacprod, SD_frac_bacprod, fracprod_per_cell_noinf)
invsimps_df <- filter(otu_alphadiv, measure == "Inverse_Simpson") %>%
dplyr::select(norep_filter_name, mean, sd, frac_bacprod, SD_frac_bacprod, fracprod_per_cell_noinf)
# Prepare to be merged with each other
unweighted_df <- unweighted_sesMPD_indepswap_RAREFIED_rm2 %>%
rownames_to_column("names") %>%
mutate(phylo_measure = "Unweighted_SESMPD") %>%
make_metadata_norep() %>%
mutate(fraction = fct_recode(fraction, Particle = "WholePart", Free = "WholeFree")) %>%
rename(norep_filter_name = names) %>%
left_join(rich_df, by = "norep_filter_name") %>%
mutate(lakesite = factor(lakesite, levels = c("MOT", "MDP","MBR", "MIN")),
lakesite = fct_recode(lakesite, Outlet = "MOT", Deep = "MDP", Bear = "MBR", River = "MIN"))
weighted_df <- WEIGHTED_sesMPD_indepswap_RAREFIED_rm2 %>%
rownames_to_column("names") %>%
mutate(phylo_measure = "Weighted_SESMPD") %>%
make_metadata_norep() %>%
mutate(fraction = fct_recode(fraction, Particle = "WholePart", Free = "WholeFree")) %>%
rename(norep_filter_name = names) %>%
left_join(invsimps_df, by = "norep_filter_name") %>%
mutate(lakesite = factor(lakesite, levels = c("MOT", "MDP","MBR", "MIN")),
lakesite = fct_recode(lakesite, Outlet = "MOT", Deep = "MDP", Bear = "MBR", River = "MIN"))Prepare the data for Ridge & Lasso Regressions
unweight_vals <- unweighted_df %>%
dplyr::select(mpd.obs.z, norep_filter_name) %>%
rename(Unweighted_PD = mpd.obs.z)
weighted_vals <- weighted_df %>%
dplyr::select(mpd.obs.z, norep_filter_name) %>%
rename(Weighted_PD = mpd.obs.z)
wide_all_divs <- otu_alphadiv %>%
dplyr::select(norep_filter_name ,mean, measure) %>%
spread(measure, mean)
lasso_data_df <- metadata_pca %>%
left_join(wide_all_divs, by = "norep_filter_name") %>%
left_join(unweight_vals, by = "norep_filter_name") %>%
left_join(weighted_vals, by = "norep_filter_name") %>%
dplyr::select(-c(project, year, Date, limnion, norep_water_name, dnaconcrep1,
SD_perc_attached_bacprod, SE_total_bac_abund, SE_perc_attached_abund, SE_attached_bac))
### PARTICLE DATA
lasso_data_df_particle <- lasso_data_df %>%
filter(fraction == "Particle" ) %>%
dplyr::select(-fraction)
lasso_data_df_particle_noprod <- lasso_data_df_particle %>%
dplyr::select(-c(fracprod_per_cell, fracprod_per_cell_noinf,
tot_bacprod, SD_tot_bacprod,SD_frac_bacprod,
norep_filter_name, BGA_cellspermL, Turb_NTU,
lakesite, season, station))
percell_lasso_data_df_particle_noprod <- lasso_data_df_particle %>%
dplyr::select(-c(fracprod_per_cell, frac_bacprod,
tot_bacprod, SD_tot_bacprod,SD_frac_bacprod,
lakesite, season, norep_filter_name, station, BGA_cellspermL, Turb_NTU)) %>%
dplyr::filter(Temp_C != 14.28) # Remove the missing row of data :( )
## ALL DATA
all_dat_lasso_comm <- lasso_data_df %>%
dplyr::select(-c(fracprod_per_cell, fracprod_per_cell_noinf,
tot_bacprod, SD_tot_bacprod,SD_frac_bacprod,
norep_filter_name, BGA_cellspermL, Turb_NTU,
lakesite, season, station, fraction))
all_dat_lasso_percapita <- lasso_data_df %>%
dplyr::select(-c(fracprod_per_cell, frac_bacprod,
tot_bacprod, SD_tot_bacprod,SD_frac_bacprod, fraction,
lakesite, season, norep_filter_name, station, BGA_cellspermL, Turb_NTU)) %>%
dplyr::filter(Temp_C != 14.28) # Remove the missing row of data :( )Multiple Regression
## Community Bulk Production: No correlation with PC2
summary(lm(frac_bacprod ~ Inverse_Simpson, data = lasso_data_df_particle)) ##
## Call:
## lm(formula = frac_bacprod ~ Inverse_Simpson, data = lasso_data_df_particle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.5589 -2.1093 -0.1969 0.8752 7.8282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.45199 2.43866 -0.185 0.85666
## Inverse_Simpson 0.29344 0.05781 5.076 0.00048 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.571 on 10 degrees of freedom
## Multiple R-squared: 0.7204, Adjusted R-squared: 0.6925
## F-statistic: 25.77 on 1 and 10 DF, p-value: 0.0004804summary(lm(frac_bacprod ~ PC2, data = lasso_data_df_particle)) ##
## Call:
## lm(formula = frac_bacprod ~ PC2, data = lasso_data_df_particle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.1121 -4.1993 0.3866 2.9143 16.5918
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.958 2.119 4.701 0.000841 ***
## PC2 -4.032 2.048 -1.969 0.077229 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.339 on 10 degrees of freedom
## Multiple R-squared: 0.2795, Adjusted R-squared: 0.2074
## F-statistic: 3.878 on 1 and 10 DF, p-value: 0.07723## Per Capita Production --> Correlations for both Simpson and PC2
summary(lm(log10(fracprod_per_cell_noinf) ~ Inverse_Simpson, data = lasso_data_df_particle)) ##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ Inverse_Simpson,
## data = lasso_data_df_particle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.28651 -0.18384 -0.11125 0.07337 0.56444
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.360961 0.159490 -46.153 5.27e-12 ***
## Inverse_Simpson 0.018087 0.003759 4.812 0.000958 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2969 on 9 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.7201, Adjusted R-squared: 0.689
## F-statistic: 23.15 on 1 and 9 DF, p-value: 0.0009581summary(lm(log10(fracprod_per_cell_noinf) ~ PC2, data = lasso_data_df_particle)) ##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ PC2, data = lasso_data_df_particle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.58949 -0.27765 0.04936 0.18633 0.87387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.6883 0.1258 -53.183 1.48e-12 ***
## PC2 -0.3385 0.1237 -2.735 0.023 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4146 on 9 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.454, Adjusted R-squared: 0.3933
## F-statistic: 7.482 on 1 and 9 DF, p-value: 0.02302summary(lm(log10(fracprod_per_cell_noinf) ~ Inverse_Simpson + PC2, data = lasso_data_df_particle)) ##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ Inverse_Simpson +
## PC2, data = lasso_data_df_particle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.32534 -0.15648 -0.06430 0.04141 0.52396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.250277 0.208543 -34.766 5.12e-10 ***
## Inverse_Simpson 0.015255 0.005087 2.999 0.0171 *
## PC2 -0.101149 0.119891 -0.844 0.4234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3017 on 8 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.7429, Adjusted R-squared: 0.6787
## F-statistic: 11.56 on 2 and 8 DF, p-value: 0.004366summary(lm(log10(fracprod_per_cell_noinf) ~ unweighted_div, data = lasso_data_df_particle)) ## Error in eval(predvars, data, env): object 'unweighted_div' not foundPerform Ridge and Lasso regression
Bulk Community Production: PARTICLE
# Set the seed for reproducibility of the grid values
set.seed(777)
################ PREPARE DATA ################
# Subset data needed and scale all o
scaled_comm_data <-
lasso_data_df_particle_noprod %>% # Use data only for the particle samples
scale() %>% # Scale all of the variables to have mean =0 and sd = 1
as.data.frame() # Make it a dataframe so that model.matrix function works (does not take a matrix)
# Set model parameters for community level data
## NOTE: there cannot be any data with NA
x = model.matrix(frac_bacprod ~ ., scaled_comm_data)[,-1]
y = scaled_comm_data$frac_bacprod
grid = 10^seq(10,-2,length = 100)
################ PREPARE TRAINING & TESTING DATA FOR CROSS VALIDATION ################
# Pull out test and training sets for cross validation
# We will use half the set to train the model and the 2nd half of the dataset to test the model
train <- sample(1:nrow(x), nrow(x)/2)
test <- -train
y_test <- y[test]
################ RIDGE ################
# Run RIDGE regression with alpha = 0
ridge_divs_train <- glmnet(x[train,], y[train], alpha = 0, lambda = grid, thresh = 1e-12, standardize = TRUE)
par(mfrow = c(1,2))
plot(ridge_divs_train)
# Cross validation
cv_ridge_divs <- cv.glmnet(x[train,], y[train], alpha = 0)
plot(cv_ridge_divs)
best_ridge_lambda <- cv_ridge_divs$lambda.min
ridge_divs_pred <- predict(ridge_divs_train, s = best_ridge_lambda, newx = x[test,])
mean((ridge_divs_pred - y_test)^2) # Test MSE## [1] 1.124927## Run ridge on the entire dataset
ridge_divs <- glmnet(x, y, alpha = 0, lambda = grid, standardize = TRUE)
par(mfrow = c(1,1))
plot(ridge_divs)
################ LASSO ################
# Run lasso regression with alpha = 1
lasso_divs_train <- glmnet(x[train,], y[train], alpha = 1, lambda = grid, standardize = TRUE)
par(mfrow = c(1,2))
plot(lasso_divs_train)
# Cross validation
cv_lasso_divs <- cv.glmnet(x[train,], y[train], alpha = 1)
plot(cv_lasso_divs)
best_lasso_lambda <- cv_lasso_divs$lambda.min
lasso_divs_pred <- predict(lasso_divs_train, s = best_lasso_lambda, newx = x[test,])
mean((lasso_divs_pred - y_test)^2)## [1] 1.405385## Run lasso on the entire dataset with the best lambda value
lasso_divs <- glmnet(x, y, alpha = 1, lambda = grid, standardize = TRUE)
par(mfrow = c(1,1))
plot(lasso_divs)
plot(lasso_divs, xvar = "lambda", label = TRUE)
plot(lasso_divs, xvar = "dev", label = TRUE)
# What are the lasso coefficients? (Anything with a . is not selected by the model)
predict(lasso_divs, type = "coefficients", s = best_lasso_lambda)## 35 x 1 sparse Matrix of class "dgCMatrix"
## 1
## (Intercept) -1.499450e-17
## Sample_depth_m .
## Temp_C .
## SpCond_uSpercm .
## TDS_mgperL .
## pH .
## ORP_mV .
## Chl_Lab_ugperL .
## Cl_mgperL .
## SO4_mgperL .
## NO3_mgperL .
## NH3_mgperL .
## TKN_mgperL .
## SRP_ugperL .
## TP_ugperL .
## Alk_mgperL .
## DO_mgperL .
## DO_percent .
## total_bac_abund .
## attached_bac .
## perc_attached_abund .
## perc_attached_bacprod .
## fraction_bac_abund .
## PC1 .
## PC2 .
## PC3 .
## PC4 .
## PC5 .
## PC6 .
## Richness .
## Shannon_Entropy .
## Inverse_Simpson 1.609355e-01
## Simpsons_Evenness .
## Unweighted_PD .
## Weighted_PD .The test MSE for ridge regression is 1.124927 while the test MSE for lasso is 1.4053855.
Additionally, the lasso model uses Inverse Simpson as the best and only predictor of production!
Per capita Production: PARTICLE
scaled_percapita_data <- percell_lasso_data_df_particle_noprod %>%
dplyr::filter(!is.na(fracprod_per_cell_noinf)) %>%
mutate(log10_percell = log10(fracprod_per_cell_noinf)) %>%
dplyr::select(-c(fracprod_per_cell_noinf)) %>%
scale() %>%
as.data.frame()
set.seed(777)
# Set model parameters for community level data
## NOTE: there cannot be any data with NA
x = model.matrix(log10_percell ~ ., scaled_percapita_data)[,-1]
y = scaled_percapita_data$log10_percell
grid = 10^seq(10,-2,length = 100)
# Pull out test and training sets for cross validation
# We will use half the set to train the model and the 2nd half of the dataset to test the model
train <- sample(1:nrow(x), nrow(x)/2)
test <- -train
y_test <- y[test]
################ RIDGE
# Run RIDGE regression with alpha = 0
ridge_divs_train <- glmnet(x[train,], y[train], alpha = 0, lambda = grid, thresh = 1e-12, standardize = FALSE)
par(mfrow = c(1,2))
plot(ridge_divs_train)
# Cross validation
cv_ridge_divs <- cv.glmnet(x[train,], y[train], alpha = 0)
plot(cv_ridge_divs)
best_ridge_lambda <- cv_ridge_divs$lambda.min
ridge_divs_pred <- predict(ridge_divs_train, s = best_ridge_lambda, newx = x[test,])
mean((ridge_divs_pred - y_test)^2) # Test MSE## [1] 1.658946## Run ridge on the entire dataset
ridge_divs <- glmnet(x, y, alpha = 0, lambda = grid, standardize = FALSE)
par(mfrow = c(1,1))
plot(ridge_divs)
################ LASSO
# Run lasso regression with alpha = 1
lasso_divs_train <- glmnet(x[train,], y[train], alpha = 1, lambda = grid, standardize = FALSE)
par(mfrow = c(1,2))
plot(lasso_divs_train)
# Cross validation
cv_lasso_divs <- cv.glmnet(x[train,], y[train], alpha = 1)
plot(cv_lasso_divs)
best_lasso_lambda <- cv_lasso_divs$lambda.min
lasso_divs_pred <- predict(lasso_divs_train, s = best_lasso_lambda, newx = x[test,])
mean((lasso_divs_pred - y_test)^2)## [1] 1.780649## Run lasso on the entire dataset
lasso_divs <- glmnet(x, y, alpha = 1, lambda = grid, standardize = FALSE)
par(mfrow = c(1,1))
plot(lasso_divs)
plot(lasso_divs, xvar = "lambda", label = TRUE)
plot(lasso_divs, xvar = "dev", label = TRUE)
# What are the lasso coefficients?
predict(lasso_divs, type = "coefficients", s = best_lasso_lambda)## 35 x 1 sparse Matrix of class "dgCMatrix"
## 1
## (Intercept) -7.288386e-16
## Sample_depth_m .
## Temp_C -1.356874e-01
## SpCond_uSpercm .
## TDS_mgperL .
## pH .
## ORP_mV .
## Chl_Lab_ugperL .
## Cl_mgperL .
## SO4_mgperL .
## NO3_mgperL .
## NH3_mgperL .
## TKN_mgperL .
## SRP_ugperL .
## TP_ugperL .
## Alk_mgperL .
## DO_mgperL .
## DO_percent .
## total_bac_abund -9.147228e-04
## attached_bac -2.317379e-02
## perc_attached_abund .
## perc_attached_bacprod .
## fraction_bac_abund .
## PC1 .
## PC2 .
## PC3 .
## PC4 .
## PC5 -2.691873e-02
## PC6 .
## Richness .
## Shannon_Entropy .
## Inverse_Simpson 4.161940e-01
## Simpsons_Evenness .
## Unweighted_PD .
## Weighted_PD .Community-wide Production: ALL SAMPLES
# Set the seed for reproducibility of the grid values
set.seed(777)
################ PREPARE DATA ################
# Subset data needed and scale all o
scaled_comm_data_ALL <-
all_dat_lasso_percapita %>% # Use data only for the particle samples
scale() %>% # Scale all of the variables to have mean =0 and sd = 1
as.data.frame() # Make it a dataframe so that model.matrix function works (does not take a matrix)
# Set model parameters for community level data
## NOTE: there cannot be any data with NA
x = model.matrix(frac_bacprod ~ ., scaled_comm_data_ALL)[,-1]## Error in eval(predvars, data, env): object 'frac_bacprod' not foundy = scaled_comm_data$frac_bacprod
grid = 10^seq(10,-2,length = 100)
################ PREPARE TRAINING & TESTING DATA FOR CROSS VALIDATION ################
# Pull out test and training sets for cross validation
# We will use half the set to train the model and the 2nd half of the dataset to test the model
train <- sample(1:nrow(x), nrow(x)/2)
test <- -train
y_test <- y[test]
################ LASSO ################
# Run lasso regression with alpha = 1
lasso_divs_train <- glmnet(x[train,], y[train], alpha = 1, lambda = grid, standardize = TRUE)
par(mfrow = c(1,2))
plot(lasso_divs_train)
# Cross validation
cv_lasso_divs <- cv.glmnet(x[train,], y[train], alpha = 1)
plot(cv_lasso_divs)
best_lasso_lambda <- cv_lasso_divs$lambda.min
lasso_divs_pred <- predict(lasso_divs_train, s = best_lasso_lambda, newx = x[test,])
mean((lasso_divs_pred - y_test)^2)## Error in mean((lasso_divs_pred - y_test)^2): dims [product 6] do not match the length of object [7]## Run lasso on the entire dataset with the best lambda value
lasso_divs <- glmnet(x, y, alpha = 1, lambda = grid, standardize = TRUE)## Error in glmnet(x, y, alpha = 1, lambda = grid, standardize = TRUE): number of observations in y (12) not equal to the number of rows of x (11)par(mfrow = c(1,1))
plot(lasso_divs)
plot(lasso_divs, xvar = "lambda", label = TRUE)
plot(lasso_divs, xvar = "dev", label = TRUE)
# What are the lasso coefficients? (Anything with a . is not selected by the model)
predict(lasso_divs, type = "coefficients", s = best_lasso_lambda)## 35 x 1 sparse Matrix of class "dgCMatrix"
## 1
## (Intercept) -5.458950e-16
## Sample_depth_m .
## Temp_C -1.639522e-01
## SpCond_uSpercm .
## TDS_mgperL .
## pH -3.298947e-02
## ORP_mV .
## Chl_Lab_ugperL .
## Cl_mgperL .
## SO4_mgperL .
## NO3_mgperL .
## NH3_mgperL .
## TKN_mgperL .
## SRP_ugperL .
## TP_ugperL .
## Alk_mgperL .
## DO_mgperL .
## DO_percent .
## total_bac_abund -9.403629e-02
## attached_bac -1.023319e-01
## perc_attached_abund .
## perc_attached_bacprod .
## fraction_bac_abund .
## PC1 .
## PC2 .
## PC3 .
## PC4 -5.335177e-03
## PC5 -1.539938e-01
## PC6 .
## Richness .
## Shannon_Entropy .
## Inverse_Simpson 4.251473e-01
## Simpsons_Evenness .
## Unweighted_PD .
## Weighted_PD -1.110181e-02Per-capita Production: ALL SAMPLES
set.seed(777)
scaled_percapita_ALL <- all_dat_lasso_percapita %>%
dplyr::filter(!is.na(fracprod_per_cell_noinf)) %>%
mutate(log10_percell = log10(fracprod_per_cell_noinf)) %>%
dplyr::select(-c(fracprod_per_cell_noinf)) %>%
scale() %>%
as.data.frame()
# Set model parameters for community level data
## NOTE: there cannot be any data with NA
x = model.matrix(log10_percell ~ ., scaled_percapita_ALL)[,-1]
y = scaled_percapita_ALL$log10_percell
grid = 10^seq(10,-2,length = 100)
# Pull out test and training sets for cross validation
# We will use half the set to train the model and the 2nd half of the dataset to test the model
train <- sample(1:nrow(x), nrow(x)/2)
test <- -train
y_test <- y[test]
################ RIDGE
# Run RIDGE regression with alpha = 0
ridge_divs_train <- glmnet(x[train,], y[train], alpha = 0, lambda = grid, thresh = 1e-12, standardize = TRUE)
par(mfrow = c(1,2))
plot(ridge_divs_train)
# Cross validation
cv_ridge_divs <- cv.glmnet(x[train,], y[train], alpha = 0)
plot(cv_ridge_divs)
best_ridge_lambda <- cv_ridge_divs$lambda.min
ridge_divs_pred <- predict(ridge_divs_train, s = best_ridge_lambda, newx = x[test,])
mean((ridge_divs_pred - y_test)^2) # Test MSE## [1] 1.034983## Run ridge on the entire dataset
ridge_divs <- glmnet(x, y, alpha = 0, lambda = grid, standardize = TRUE)
par(mfrow = c(1,1))
plot(ridge_divs)
################ LASSO
# Run lasso regression with alpha = 1
lasso_divs_train <- glmnet(x[train,], y[train], alpha = 1, lambda = grid, standardize = TRUE)
par(mfrow = c(1,2))
plot(lasso_divs_train)
# Cross validation
cv_lasso_divs <- cv.glmnet(x[train,], y[train], alpha = 1)
plot(cv_lasso_divs)
best_lasso_lambda <- cv_lasso_divs$lambda.min
lasso_divs_pred <- predict(lasso_divs_train, s = best_lasso_lambda, newx = x[test,])
mean((lasso_divs_pred - y_test)^2)## [1] 0.7951617## Run lasso on the entire dataset
lasso_divs <- glmnet(x, y, alpha = 1, lambda = grid, standardize = TRUE)
par(mfrow = c(1,1))
plot(lasso_divs)
plot(lasso_divs, xvar = "lambda", label = TRUE)
plot(lasso_divs, xvar = "dev", label = TRUE)
# What are the lasso coefficients?
predict(lasso_divs, type = "coefficients", s = best_lasso_lambda)## 35 x 1 sparse Matrix of class "dgCMatrix"
## 1
## (Intercept) 8.490670e-16
## Sample_depth_m .
## Temp_C .
## SpCond_uSpercm .
## TDS_mgperL .
## pH -1.566237e-01
## ORP_mV .
## Chl_Lab_ugperL .
## Cl_mgperL .
## SO4_mgperL -2.666180e-03
## NO3_mgperL .
## NH3_mgperL .
## TKN_mgperL .
## SRP_ugperL .
## TP_ugperL .
## Alk_mgperL .
## DO_mgperL .
## DO_percent .
## total_bac_abund .
## attached_bac .
## perc_attached_abund .
## perc_attached_bacprod .
## fraction_bac_abund -1.327209e-01
## PC1 .
## PC2 .
## PC3 .
## PC4 .
## PC5 .
## PC6 .
## Richness 3.769169e-01
## Shannon_Entropy .
## Inverse_Simpson .
## Simpsons_Evenness .
## Unweighted_PD -7.126563e-02
## Weighted_PD .Figure 3: Unweighted ses.mpd
######################################################### SES MPD DISTRIBUTION: UNWEIGHTED
unweighted_fraction_wilcox <- wilcox.test(mpd.obs.z ~ fraction, data = unweighted_df)
unweighted_fraction_wilcox##
## Wilcoxon rank sum test
##
## data: mpd.obs.z by fraction
## W = 30, p-value = 0.01449
## alternative hypothesis: true location shift is not equal to 0unweighted_df %>%
group_by(fraction) %>%
summarize(mean(mpd.obs.z))## # A tibble: 2 x 2
## fraction `mean(mpd.obs.z)`
## <fctr> <dbl>
## 1 Particle -0.4842762
## 2 Free 0.4280979# Make a data frame to draw significance line in boxplot (visually calculated)
dat4 <- data.frame(a = c(1.15,1.15,1.85,1.85), b = c(1.6,1.7,1.7,1.6)) # WholePart vs WholeFree
unweight_distribution_plot <-
ggplot(unweighted_df, aes(y = mpd.obs.z, x = fraction)) +
#scale_color_manual(values = fraction_colors) +
scale_fill_manual(values = fraction_colors) +
geom_jitter(size = 3, aes(fill = fraction, shape = season), width = 0.2) +
geom_boxplot(alpha = 0.5, outlier.shape = NA, aes(fill = fraction)) +
scale_shape_manual(values = season_shapes) +
scale_y_continuous(limits = c(-1.5,1.75), breaks = seq(from = -1.5, to = 1.5, by = 0.5)) +
ylab("Standardized Effect Size \n Unweighted Mean Pairwise Dist") +
xlab("Fraction") +
geom_hline(yintercept = 0, linetype = "dashed", size = 1.5) +
##### Particle vs free per-cell production
geom_path(data = dat4, aes(x = a, y = b), linetype = 1, color = "#424645") +
annotate("text", x=1.5, y=1.35, fontface = "bold", size = 4, color = "#424645",
label= paste("p =", round(unweighted_fraction_wilcox$p.value, digits = 2))) +
theme(legend.position = "none",# axis.title.y = element_blank(),
axis.title.x = element_blank(), axis.text.x = element_blank()) +
coord_flip()
# Is there a relationship between richness and Unweighted Mean Pairwise distance?
summary(lm(mean ~ mpd.obs.z, data = unweighted_df))##
## Call:
## lm(formula = mean ~ mpd.obs.z, data = unweighted_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -194.03 -100.85 -10.60 84.21 294.14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 444.12 28.28 15.703 1.95e-13 ***
## mpd.obs.z -124.30 34.19 -3.636 0.00146 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 138.5 on 22 degrees of freedom
## Multiple R-squared: 0.3754, Adjusted R-squared: 0.347
## F-statistic: 13.22 on 1 and 22 DF, p-value: 0.001459summary(lm(mean ~ mpd.obs.z, data = filter(unweighted_df, fraction == "Particle")))##
## Call:
## lm(formula = mean ~ mpd.obs.z, data = filter(unweighted_df, fraction ==
## "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -189.00 -106.35 -44.10 58.05 274.78
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 515.62 50.25 10.261 1.25e-06 ***
## mpd.obs.z -85.02 48.76 -1.744 0.112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 153.7 on 10 degrees of freedom
## Multiple R-squared: 0.2332, Adjusted R-squared: 0.1565
## F-statistic: 3.041 on 1 and 10 DF, p-value: 0.1118summary(lm(mean ~ mpd.obs.z, data = filter(unweighted_df, fraction == "Free")))##
## Call:
## lm(formula = mean ~ mpd.obs.z, data = filter(unweighted_df, fraction ==
## "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -100.20 -66.23 -17.30 43.28 172.40
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 338.3215 41.0307 8.246 9.03e-06 ***
## mpd.obs.z 0.2398 74.0807 0.003 0.997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 90.18 on 10 degrees of freedom
## Multiple R-squared: 1.048e-06, Adjusted R-squared: -0.1
## F-statistic: 1.048e-05 on 1 and 10 DF, p-value: 0.9975unweight_rich_vs_mpd_plot <-
ggplot(unweighted_df, aes(y = mean, x = mpd.obs.z)) +
geom_errorbar(aes(ymin = mean - sd, ymax = mean + sd, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_vline(xintercept = 0, linetype = "dashed", size = 1.5) +
geom_point(size = 3, aes(fill = fraction, shape = season)) + ylab("\n Observed Richness") +
scale_shape_manual(values = season_shapes) +
xlab("Standardized Effect Size \n Unweighted Mean Pairwise Dist") +
scale_fill_manual(values = fraction_colors) +
geom_smooth(method = "lm", color = "#424645", fill = "#424645", alpha = 0.3) +
scale_x_continuous(limits = c(-1.5,1.75), breaks = seq(from = -1.5, to = 1.5, by = 0.5)) +
annotate("text", x = 0.75, y=750, color = "#424645", fontface = "bold",
label = paste("R2 =", round(summary(lm(mean ~ mpd.obs.z, data = unweighted_df))$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm(mean ~ mpd.obs.z, data = unweighted_df))$coefficients[,4][2]), digits = 3))) +
theme(legend.title = element_blank(), legend.position = "none",
axis.text.x = element_blank(), axis.title.x = element_blank())
# Is there a relationship between Production and Unweighted Mean Pairwise distance?
#summary(lm(frac_bacprod ~ mpd.obs.z, data = unweighted_df)) # NS
summary(lm(frac_bacprod ~ mpd.obs.z, data = filter(unweighted_df, fraction == "Particle")))##
## Call:
## lm(formula = frac_bacprod ~ mpd.obs.z, data = filter(unweighted_df,
## fraction == "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.230 -4.036 -1.734 3.173 19.457
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.247 2.582 3.194 0.00959 **
## mpd.obs.z -3.533 2.505 -1.410 0.18882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.896 on 10 degrees of freedom
## Multiple R-squared: 0.1659, Adjusted R-squared: 0.08246
## F-statistic: 1.989 on 1 and 10 DF, p-value: 0.1888summary(lm(frac_bacprod ~ mpd.obs.z, data = filter(unweighted_df, fraction == "Free")))##
## Call:
## lm(formula = frac_bacprod ~ mpd.obs.z, data = filter(unweighted_df,
## fraction == "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.508 -14.439 1.956 8.581 37.337
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.989 8.104 2.343 0.0411 *
## mpd.obs.z 11.841 14.632 0.809 0.4372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.81 on 10 degrees of freedom
## Multiple R-squared: 0.06146, Adjusted R-squared: -0.0324
## F-statistic: 0.6548 on 1 and 10 DF, p-value: 0.4372unweight_prod_vs_mpd_plot <-
ggplot(unweighted_df, aes(y = frac_bacprod, x = mpd.obs.z, fill = fraction)) +
geom_errorbar(aes(ymin = frac_bacprod - SD_frac_bacprod, ymax = frac_bacprod + SD_frac_bacprod, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_vline(xintercept = 0, linetype = "dashed", size = 1.5) +
geom_point(size = 3, aes(shape = season)) +
scale_shape_manual(values = season_shapes) +
ylab("\n Community Production \n (μgC/L/day)") +
xlab("Standardized Effect Size \n Unweighted Mean Pairwise Dist") +
scale_fill_manual(values = fraction_colors) +
scale_x_continuous(limits = c(-1.5,1.75), breaks = seq(from = -1.5, to = 1.5, by = 0.5)) +
theme(legend.title = element_blank(), legend.position = "none",
axis.text.x = element_blank(), axis.title.x = element_blank())
# Is there a relationship between PER-CELL PRODUCTION and Unweighted Mean Pairwise distance?
unweight_vs_percell <- lm(log10(fracprod_per_cell_noinf) ~ mpd.obs.z, data = unweighted_df)
summary(unweight_vs_percell)##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mpd.obs.z, data = unweighted_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.69739 -0.28558 0.00112 0.11739 1.31840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.19827 0.09996 -72.011 < 2e-16 ***
## mpd.obs.z -0.49468 0.12004 -4.121 0.000487 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4782 on 21 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.4471, Adjusted R-squared: 0.4208
## F-statistic: 16.98 on 1 and 21 DF, p-value: 0.0004866summary(lm(log10(fracprod_per_cell_noinf) ~ mpd.obs.z, data = filter(unweighted_df, fraction == "Particle")))##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mpd.obs.z, data = filter(unweighted_df,
## fraction == "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.38323 -0.23870 -0.19095 0.05971 1.17646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.9197 0.1684 -41.090 1.49e-11 ***
## mpd.obs.z -0.3279 0.1595 -2.056 0.0699 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4628 on 9 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3196, Adjusted R-squared: 0.244
## F-statistic: 4.228 on 1 and 9 DF, p-value: 0.0699summary(lm(log10(fracprod_per_cell_noinf) ~ mpd.obs.z, data = filter(unweighted_df, fraction == "Free")))##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mpd.obs.z, data = filter(unweighted_df,
## fraction == "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.62822 -0.30045 0.09582 0.16090 0.77191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.54050 0.18773 -40.167 2.19e-12 ***
## mpd.obs.z -0.08069 0.33894 -0.238 0.817
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4126 on 10 degrees of freedom
## Multiple R-squared: 0.005636, Adjusted R-squared: -0.0938
## F-statistic: 0.05668 on 1 and 10 DF, p-value: 0.8166unweight_percell_vs_mpd_plot <-
ggplot(unweighted_df,
aes(y = log10(fracprod_per_cell_noinf), x = mpd.obs.z)) +
geom_vline(xintercept = 0, linetype = "dashed", size = 1.5) +
geom_point(size = 3, aes(fill = fraction, shape = season)) +
ylab("\n log10(Per-Capita Production)\n (μgC/cell/day)") +
xlab("Unweighted Phylogenetic Diversity") +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
geom_smooth(method = "lm", color="#424645", fill = "#424645", alpha = 0.3) +
#stat_smooth(method="lm", se=TRUE, formula= y ~ poly(x, 2, raw=TRUE), color="#424645", fill = "#424645", alpha = 0.3) +
scale_y_continuous(limits = c(-8.5,-5), breaks = c(-8, -7, -6)) +
scale_x_continuous(limits = c(-1.5,1.75), breaks = seq(from = -1.5, to = 1.5, by = 0.5)) +
annotate("text", x = 0.75, y=-6, color = "#424645", fontface = "bold",
label = paste("R2 =", round(summary(unweight_vs_percell)$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(unweight_vs_percell)$coefficients[,4][2]), digits = 4))) +
theme(legend.title = element_blank(), legend.position ="bottom",
legend.text = element_text(size = 14))
######## FIGURE 3
figure3_row1 <- plot_grid(unweight_distribution_plot, unweight_rich_vs_mpd_plot, unweight_prod_vs_mpd_plot,
unweight_percell_vs_mpd_plot + theme(legend.position = "none"),
labels = c("A", "B", "C", "D"), ncol = 1, nrow = 4,
rel_heights = c(0.5, 1, 1, 1.2),
align = "v")
plot_grid(figure3_row1, season_legend,
ncol = 1, nrow = 2,
rel_heights = c(1, 0.05))
Figure S6: Weighted ses.mpd
######################################################### SES MPD DISTRIBUTION: WEIGHTED
weighted_fraction_wilcox <- wilcox.test(mpd.obs.z ~ fraction, data = weighted_df)
weighted_fraction_wilcox##
## Wilcoxon rank sum test
##
## data: mpd.obs.z by fraction
## W = 80, p-value = 0.6707
## alternative hypothesis: true location shift is not equal to 0filter(weighted_df) %>%
group_by(fraction) %>%
summarize(mean(mpd.obs.z))## # A tibble: 2 x 2
## fraction `mean(mpd.obs.z)`
## <fctr> <dbl>
## 1 Particle -0.3484573
## 2 Free -0.3877349# Make a data frame to draw significance line in boxplot (visually calculated)
dat5 <- data.frame(a = c(1.15,1.15,1.85,1.85), b = c(1.6,1.7,1.7,1.6)) # WholePart vs WholeFree
weight_distribution_plot <-
ggplot(weighted_df, aes(y = mpd.obs.z, x = fraction)) +
#scale_color_manual(values = fraction_colors) +
scale_fill_manual(values = fraction_colors) +
geom_jitter(size = 3.5, aes(fill = fraction, shape = season), width = 0.2) +
geom_boxplot(alpha = 0.5, outlier.shape = NA, aes(fill = fraction)) +
scale_shape_manual(values = season_shapes) +
scale_y_continuous(limits = c(-1.5,1.75), breaks = seq(from = -1.5, to = 1.5, by = 0.5)) +
ylab("Standardized Effect Size \n Unweighted Mean Pairwise Dist") +
xlab("Fraction") +
geom_hline(yintercept = 0, linetype = "dashed", size = 1.5) +
##### Particle vs free per-cell production
geom_path(data = dat5, aes(x = a, y = b), linetype = 1, color = "#424645") +
annotate("text", x=1.5, y=1.55, fontface = "bold", size = 3.5, color = "#424645", label= "NS") +
theme(legend.position = "none", #axis.title.y = element_blank(),
axis.title.x = element_blank(), axis.text.x = element_blank()) +
coord_flip()
# Is there a relationship between inverse simpson and Weighted Mean Pairwise distance?
#summary(lm(mean ~ mpd.obs.z, data = weighted_df)) # NS
summary(lm(mean ~ mpd.obs.z, data = filter(weighted_df, fraction == "Particle")))##
## Call:
## lm(formula = mean ~ mpd.obs.z, data = filter(weighted_df, fraction ==
## "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.184 -20.664 0.742 13.835 39.244
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.32 12.24 1.904 0.086 .
## mpd.obs.z -34.90 29.31 -1.191 0.261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.4 on 10 degrees of freedom
## Multiple R-squared: 0.1242, Adjusted R-squared: 0.03661
## F-statistic: 1.418 on 1 and 10 DF, p-value: 0.2612summary(lm(mean ~ mpd.obs.z, data = filter(weighted_df, fraction == "Free")))##
## Call:
## lm(formula = mean ~ mpd.obs.z, data = filter(weighted_df, fraction ==
## "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.0180 -4.2707 -0.7356 3.6842 18.1340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.787 4.868 4.475 0.00119 **
## mpd.obs.z -5.945 10.883 -0.546 0.59683
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.409 on 10 degrees of freedom
## Multiple R-squared: 0.02898, Adjusted R-squared: -0.06812
## F-statistic: 0.2984 on 1 and 10 DF, p-value: 0.5968weight_invsimps_vs_mpd_plot <-
ggplot(weighted_df, aes(y = mean, x = mpd.obs.z, fill = fraction)) +
geom_errorbar(aes(ymin = mean - sd, ymax = mean + sd, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_vline(xintercept = 0, linetype = "dashed", size = 1.5) +
geom_point(size = 3.5, aes(shape = season)) + ylab("Inverse Simpson") +
scale_shape_manual(values = season_shapes) +
#xlab("Standardized Effect Size \n Weighted Mean Pairwise Dist") +
scale_fill_manual(values = fraction_colors) +
scale_x_continuous(limits = c(-1.5,1.75), breaks = seq(from = -1.5, to = 1.5, by = 0.5)) +
theme(legend.title = element_blank(), legend.position = "none",
axis.text.x = element_blank(), axis.title.x = element_blank())
# Is there a relationship between PER-LITER PRODUCTION and WEIGHTED Mean Pairwise distance?
summary(lm(frac_bacprod ~ mpd.obs.z, data = weighted_df))##
## Call:
## lm(formula = frac_bacprod ~ mpd.obs.z, data = weighted_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.495 -9.462 -2.031 5.401 38.918
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.225 5.711 1.615 0.120
## mpd.obs.z -21.146 13.195 -1.603 0.123
##
## Residual standard error: 14.72 on 22 degrees of freedom
## Multiple R-squared: 0.1045, Adjusted R-squared: 0.06383
## F-statistic: 2.568 on 1 and 22 DF, p-value: 0.1233summary(lm(frac_bacprod ~ mpd.obs.z, data = filter(weighted_df, fraction == "Particle")))##
## Call:
## lm(formula = frac_bacprod ~ mpd.obs.z, data = filter(weighted_df,
## fraction == "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.986 -4.879 -1.114 5.303 15.080
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.601 4.042 1.138 0.281
## mpd.obs.z -15.374 9.673 -1.589 0.143
##
## Residual standard error: 7.725 on 10 degrees of freedom
## Multiple R-squared: 0.2016, Adjusted R-squared: 0.1218
## F-statistic: 2.526 on 1 and 10 DF, p-value: 0.1431summary(lm(frac_bacprod ~ mpd.obs.z, data = filter(weighted_df, fraction == "Free")))##
## Call:
## lm(formula = frac_bacprod ~ mpd.obs.z, data = filter(weighted_df,
## fraction == "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.530 -14.203 -3.927 7.777 31.965
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.50 10.18 1.523 0.159
## mpd.obs.z -22.07 22.75 -0.970 0.355
##
## Residual standard error: 17.58 on 10 degrees of freedom
## Multiple R-squared: 0.08605, Adjusted R-squared: -0.005349
## F-statistic: 0.9415 on 1 and 10 DF, p-value: 0.3548weight_prod_vs_mpd_plot <-
ggplot(weighted_df, aes(y = frac_bacprod, x = mpd.obs.z, fill = fraction)) +
geom_errorbar(aes(ymin = frac_bacprod - SD_frac_bacprod, ymax = frac_bacprod + SD_frac_bacprod, color = fraction), alpha = 0.7) + # X-axis errorbars
geom_vline(xintercept = 0, linetype = "dashed", size = 1.5) +
geom_point(size = 3.5, aes(shape = season)) +
scale_shape_manual(values = season_shapes) +
ylab("Heterotrophic Production \n (μgC/L/day)") +
xlab("Standardized Effect Size \n Weighted Mean Pairwise Dist") +
scale_fill_manual(values = fraction_colors) +
scale_x_continuous(limits = c(-1.5,1.75), breaks = seq(from = -1.5, to = 1.5, by = 0.5)) +
theme(legend.title = element_blank(), legend.position = "none",
axis.text.x = element_blank(), axis.title.x = element_blank())
# Is there a relationship between PER-CELL PRODUCTION and WEIGHTED Mean Pairwise distance?
summary(lm(log10(fracprod_per_cell_noinf) ~ mpd.obs.z, data = weighted_df))##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mpd.obs.z, data = weighted_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.94479 -0.39205 -0.05774 0.34127 1.39772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.5104 0.2485 -30.222 <2e-16 ***
## mpd.obs.z -0.8987 0.5626 -1.597 0.125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6073 on 21 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1083, Adjusted R-squared: 0.06587
## F-statistic: 2.551 on 1 and 21 DF, p-value: 0.1251summary(lm(log10(fracprod_per_cell_noinf) ~ mpd.obs.z, data = filter(weighted_df, fraction == "Particle")))##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mpd.obs.z, data = filter(weighted_df,
## fraction == "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.64641 -0.31219 0.02897 0.23609 0.91465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.0955 0.2908 -24.400 1.56e-09 ***
## mpd.obs.z -0.9946 0.6678 -1.489 0.171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5026 on 9 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1977, Adjusted R-squared: 0.1086
## F-statistic: 2.218 on 1 and 9 DF, p-value: 0.1706lm_percell_free_mpd <- lm(log10(fracprod_per_cell_noinf) ~ mpd.obs.z, data = filter(weighted_df, fraction == "Free"))
summary(lm_percell_free_mpd)##
## Call:
## lm(formula = log10(fracprod_per_cell_noinf) ~ mpd.obs.z, data = filter(weighted_df,
## fraction == "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.53303 -0.19763 0.01741 0.18356 0.46425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.9398 0.1992 -39.865 2.36e-12 ***
## mpd.obs.z -0.9408 0.4452 -2.113 0.0607 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.344 on 10 degrees of freedom
## Multiple R-squared: 0.3087, Adjusted R-squared: 0.2395
## F-statistic: 4.465 on 1 and 10 DF, p-value: 0.06074weight_percell_vs_mpd_plot <-
ggplot(weighted_df,
aes(y = log10(fracprod_per_cell_noinf), x = mpd.obs.z, fill = fraction)) +
geom_vline(xintercept = 0, linetype = "dashed", size = 1.5) +
geom_point(size = 3.5, aes(shape = season)) +
scale_shape_manual(values = season_shapes) +
ylab("log10(Production/cell)\n (μgC/cell/day)") +
xlab("Weighted Phylogenetic Diversity") +
scale_fill_manual(values = fraction_colors) +
geom_smooth(method = "lm", data = filter(weighted_df, fraction == "Free"), color = "skyblue", fill = "skyblue", alpha = 0.3) +
scale_x_continuous(limits = c(-1.5,1.75), breaks = seq(from = -1.5, to = 1.5, by = 0.5)) +
scale_y_continuous(limits = c(-8.5,-5), breaks = c(-8, -7, -6)) +
annotate("text", x = 0.65, y=-7.9, color = "skyblue", fontface = "bold",
label = paste("R2 =", round(summary(lm_percell_free_mpd)$adj.r.squared, digits = 3), "\n",
"p =", round(unname(summary(lm_percell_free_mpd)$coefficients[,4][2]), digits = 3))) +
theme(legend.title = element_blank(), legend.position ="none",
legend.text = element_text(size = 14))
row1_weighted_PD_plot <- plot_grid(weight_distribution_plot, weight_invsimps_vs_mpd_plot, weight_prod_vs_mpd_plot, weight_percell_vs_mpd_plot,
labels = c("A", "B", "C", "D"), ncol = 1, nrow = 4,
rel_heights = c(0.5, 0.8, 0.8, 1.2),
align = "v")
plot_grid(row1_weighted_PD_plot, season_legend,
ncol = 1, nrow = 2,
rel_heights = c(1, 0.05))
Cell counts and Unweighted SESmpd
# Combine the datasets into
cell_nums <- otu_alphadiv %>%
dplyr::select(norep_filter_name, fraction_bac_abund) %>%
distinct()
unweight_cellnums <- cell_nums %>%
left_join(unweighted_df, by = "norep_filter_name") %>%
dplyr::filter(norep_filter_name != "MOTEJ515")
# Is there a relationship between cell numbers and Unweighted Mean Pairwise distance?
summary(lm(log10(fraction_bac_abund) ~ mpd.obs.z, data = filter(unweight_cellnums)))##
## Call:
## lm(formula = log10(fraction_bac_abund) ~ mpd.obs.z, data = filter(unweight_cellnums))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4265 -0.2037 0.1391 0.3607 0.7260
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.2626 0.1134 46.397 < 2e-16 ***
## mpd.obs.z 0.5132 0.1362 3.767 0.00113 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5426 on 21 degrees of freedom
## Multiple R-squared: 0.4033, Adjusted R-squared: 0.3749
## F-statistic: 14.19 on 1 and 21 DF, p-value: 0.001131summary(lm(log10(fraction_bac_abund) ~ mpd.obs.z, data = filter(unweight_cellnums, fraction == "Particle")))##
## Call:
## lm(formula = log10(fraction_bac_abund) ~ mpd.obs.z, data = filter(unweight_cellnums,
## fraction == "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.60003 -0.11678 0.05996 0.23219 0.35154
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6124 0.1147 40.212 1.81e-11 ***
## mpd.obs.z 0.0637 0.1086 0.587 0.572
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3152 on 9 degrees of freedom
## Multiple R-squared: 0.03682, Adjusted R-squared: -0.0702
## F-statistic: 0.344 on 1 and 9 DF, p-value: 0.5719summary(lm(log10(fraction_bac_abund) ~ mpd.obs.z, data = filter(unweight_cellnums, fraction == "Free")))##
## Call:
## lm(formula = log10(fraction_bac_abund) ~ mpd.obs.z, data = filter(unweight_cellnums,
## fraction == "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.29232 -0.07382 -0.01386 0.12210 0.21771
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.76631 0.07691 74.974 4.35e-15 ***
## mpd.obs.z 0.15953 0.13886 1.149 0.277
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.169 on 10 degrees of freedom
## Multiple R-squared: 0.1166, Adjusted R-squared: 0.02825
## F-statistic: 1.32 on 1 and 10 DF, p-value: 0.2774ggplot(unweight_cellnums, aes(y = log10(fraction_bac_abund), x = mpd.obs.z)) +
geom_vline(xintercept = 0, linetype = "dashed", size = 1.5) +
geom_point(size = 3, shape = 21, aes(fill = fraction)) +
ylab("log10(bacterial cells/mL)") +
xlab("Standardized Effect Size \n Unweighted Mean Pairwise Dist") +
scale_fill_manual(values = fraction_colors) +
geom_smooth(method = "lm", color = "#424645", fill = "#424645", alpha = 0.3) +
scale_x_continuous(limits = c(-1.5,1.75), breaks = seq(from = -1.5, to = 1.5, by = 0.5)) +
annotate("text", x = 0.75, y=4.25, color = "#424645", fontface = "bold",
label = paste("R2 =", round(summary(lm(log10(fraction_bac_abund) ~ mpd.obs.z, data = unweight_cellnums))$adj.r.squared, digits = 2), "\n",
"p =", round(unname(summary(lm(log10(fraction_bac_abund) ~ mpd.obs.z, data = unweight_cellnums))$coefficients[,4][2]), digits = 3))) +
theme(legend.title = element_blank(), legend.position = c(0.12, 0.9))
# Test by station
part_unweighted_df <- filter(unweighted_df, fraction == "Particle")
kruskal.test(mpd.obs.z ~ lakesite, data = filter(unweighted_df, fraction == "Particle"))
unweighted_df %>%
filter(fraction == "Particle") %>%
group_by(lakesite) %>%
summarize(mean(mpd.obs.z), sd(mpd.obs.z))
# Lakesite
plot_part_unweight_lakesite <- ggplot(filter(unweighted_df, fraction == "Particle"),
aes(y = mpd.obs.z, x = lakesite)) +
ggtitle("Particle-Associated Samples Only") +
scale_fill_manual(values = lakesite_colors) +
ylab("Standardized Effect Size \n Unweighted Mean Pairwise Dist") +
geom_jitter(size = 3, shape = 21, aes(fill = lakesite), width = 0.2) +
geom_boxplot(aes(fill = lakesite), alpha = 0.5) +
theme(axis.title.x = element_blank(),
legend.position = c(0.85, 0.9), legend.title = element_blank())
# Season
plot_part_unweight_season <- ggplot(filter(unweighted_df, fraction == "Particle"),
aes(y = mpd.obs.z, x = season)) +
ggtitle("Particle-Associated Samples Only") +
ylab("Standardized Effect Size \n Unweighted Mean Pairwise Dist") +
scale_fill_manual(values = season_colors) +
geom_jitter(size = 3, shape = 21, aes(fill = season), width = 0.2) +
geom_boxplot(aes(fill = season), alpha = 0.5) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(),
legend.position = c(0.9, 0.9), legend.title = element_blank())
plot_grid(plot_part_unweight_lakesite, plot_part_unweight_season,
labels = c("A", "B"), ncol = 2, nrow = 1, align = "h")
plot_station_rich <-
ggplot(filter(otu_alphadiv, measure == "Richness" & fraction == "Particle"),
aes(x = lakesite, y = mean, fill = lakesite)) +
geom_jitter(size = 3, shape = 21) +
ylab("Observed Richness") +
ggtitle("Particle Fraction Only") +
geom_boxplot(outlier.shape = NA, color = "black", alpha = 0.5) +
scale_fill_manual(values = lakesite_colors) +
theme(legend.title = element_blank(),
legend.position = c(0.12, 0.9))
otu_alphadiv %>%
filter(measure == "Richness" & fraction == "Particle") %>%
group_by(lakesite) %>%
summarize(mean(mean), sd(mean))
plot_station_invsimps <-
ggplot(filter(otu_alphadiv, measure == "Inverse_Simpson" & fraction == "Particle"),
aes(x = lakesite, y = mean, fill = lakesite)) +
geom_jitter(size = 3, shape = 21) +
ggtitle("Particle Fraction Only") +
ylab("Inverse Simpson") +
geom_boxplot(outlier.shape = NA, color = "black", alpha = 0.5) +
scale_fill_manual(values = lakesite_colors) +
theme(legend.title = element_blank(),
legend.position = "none")
otu_alphadiv %>%
filter(measure == "Inverse_Simpson" & fraction == "Particle") %>%
group_by(lakesite) %>%
summarize(mean(mean))
plot_grid(plot_station_rich, plot_station_invsimps,
labels = c("A", "B"), ncol = 2, nrow = 1, align = "h")
#### FREE LIVING
plot_station_rich_FL <-
ggplot(filter(otu_alphadiv, measure == "Richness" & fraction == "Free"),
aes(x = lakesite, y = mean, fill = lakesite)) +
geom_jitter(size = 3, shape = 21) +
ylab("Observed Richness") +
ggtitle("Free-Living Fraction Only") +
geom_boxplot(outlier.shape = NA, color = "black", alpha = 0.5) +
scale_fill_manual(values = lakesite_colors) +
theme(legend.title = element_blank(),
legend.position = c(0.12, 0.9))
otu_alphadiv %>%
filter(measure == "Richness" & fraction == "Free") %>%
group_by(lakesite) %>%
summarize(mean(mean), sd(mean))
plot_station_invsimps_FL <-
ggplot(filter(otu_alphadiv, measure == "Inverse_Simpson" & fraction == "Free"),
aes(x = lakesite, y = mean, fill = lakesite)) +
geom_jitter(size = 3, shape = 21) +
ggtitle("Free-Living Fraction Only") +
ylab("Inverse Simpson") +
geom_boxplot(outlier.shape = NA, color = "black", alpha = 0.5) +
scale_fill_manual(values = lakesite_colors) +
theme(legend.title = element_blank(),
legend.position = "none")
otu_alphadiv %>%
filter(measure == "Inverse_Simpson" & fraction == "Free") %>%
group_by(lakesite) %>%
summarize(mean(mean))
plot_grid(plot_station_rich_FL, plot_station_invsimps_FL,
labels = c("A", "B"), ncol = 2, nrow = 1, align = "h")Figure SX: Compare Diversity Estimates
long_div_df <- lasso_data_df %>%
dplyr::select(norep_filter_name, lakesite:season, Richness:Weighted_PD) %>%
gather(measure, mean, Richness:Weighted_PD) %>%
mutate(measure = factor(measure, levels =
c("Richness", "Shannon_Entropy", "Inverse_Simpson", "Simpsons_Evenness", "Unweighted_PD", "Weighted_PD")))
divs_PAFLA_plot_lakesite <- ggplot(long_div_df, aes(y = mean, x = lakesite, shape = lakesite)) +
ylab("Mean Diversity Value\n By Lake Station") +
facet_wrap(~measure, scale = "free_y", ncol = 6) +
geom_point(size = 3, aes(fill = fraction), color = "black", position = position_jitterdodge()) +
geom_boxplot(alpha = 0.5, outlier.shape = NA, color = "black", aes(fill = fraction)) +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = lakesite_shapes) +
scale_color_manual(values = fraction_colors) +
theme(legend.position = "none", axis.title.x = element_blank(),
axis.text.x = element_text(angle = 30, vjust = 1, hjust = 1))
plot_A <- plot_grid(divs_PAFLA_plot_lakesite, lakesite_legend,
nrow = 2, ncol = 1, labels = c("A",""),
rel_heights = c(1, 0.05))
divs_PAFLA_plot_season <- ggplot(long_div_df, aes(y = mean, x = season, shape = season)) +
ylab("Mean Diversity Value \n By Season") +
facet_wrap(~measure, scale = "free_y", ncol = 6) +
geom_point(size = 3, aes(fill = fraction), color = "black", position = position_jitterdodge()) +
geom_boxplot(alpha = 0.5, outlier.shape = NA, color = "black", aes(fill = fraction)) +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
scale_color_manual(values = fraction_colors) +
theme(legend.position = "none", axis.title.x = element_blank(),
axis.text.x = element_text(angle = 30, vjust = 1, hjust = 1))
plot_B <- plot_grid(divs_PAFLA_plot_season, season_legend,
nrow = 2, ncol = 1, labels = c("B",""),
rel_heights = c(1, 0.05))
plot_grid(plot_A, plot_B,
nrow = 2, ncol = 1)
Prepare Figure S5: Randomized Richness
######################################################### Subset only richness data
### These are the richness values for the fake samples
#rich_stats <- filter(otu_alphadiv, measure == "Richness") %>%
# dplyr::select(1:2) %>%
# rename(mean_richness = mean) %>%
# mutate(sample = paste("Sample_", seq(1:nrow(filter(otu_alphadiv, measure == "Richness"))), sep = ""),
# mean_richness = matround(mean_richness))
## Pick OTUs to match these richness values
# List the otus from ALL samples
# all_otus <- taxa_names(surface_PAFL_otu_pruned_rm2)
# Obtain the OTU table from the phyloseq object
# otutab <- otu_table(surface_PAFL_otu_pruned_rm2)
# Make all the counts to be 0
# otutab_newvals <- apply(otutab, c(1, 2), function(x) 0)
# Stop if things are wrong
# stopifnot(colnames(otutab_newvals) == all_otus) # Do the OTU names match?
# stopifnot(rownames(otutab_newvals) == rich_stats$norep_filter_name) # Do the sample names match?
# Make it reproducible!
#set.seed(777)
#for (row in 1:nrow(rich_stats)) {
# Pick the richness value
# rich_val <- rich_stats[row, 2]
# Sample the OTUs to represent that richness value
# col_index <- sample(ncol(otutab_newvals), rich_val, replace = FALSE, prob = NULL)
# make all other columns 0
# otutab_newvals[row, col_index] <- 1
#}
## Calculate the tree for those randomized samples
# create a new phyloseq object
#random_physeq_presab_raw <- phyloseq(otu_table(otutab_newvals, taxa_are_rows = FALSE),
# tax_table(surface_PAFL_otu_pruned_rm2), sample_data(surface_PAFL_otu_pruned_rm2))
#random_physeq_presab_raw
# Remove taxa that are 0!
#random_physeq_presab_pruned <- prune_taxa(taxa_sums(random_physeq_presab_raw) > 0, random_physeq_presab_raw)
#random_physeq_presab_pruned
# Calculate tree
# Obtain the OTU names that were retained in the dataset
#tax <- data.frame(tax_table(random_physeq_presab_pruned))
#vector_of_otus <- as.vector(tax$OTU)
# Write out the file for processing/fasttree
# write(vector_of_otus, file = "data/PhyloTree/randomized/random_physeq_presab_pruned_OTUnames.txt", ncolumns = 1, append = FALSE, sep = "\n")Plot Figure S5: Randomized Richness
# Read in the tree
randomized_tree <- read.tree(file = "data/PhyloTree/randomized/newick_tree_randomized_rmN.tre")
#random_physeq_presab_pruned_tree <- merge_phyloseq(random_physeq_presab_pruned, randomized_tree)
#random_physeq_presab_pruned_tree
#save(list="random_physeq_presab_pruned_tree", file=paste0("data/PhyloTree/randomized/random_physeq_presab_pruned_tree.RData"))
load("data/PhyloTree/randomized/random_physeq_presab_pruned_tree.RData")
random_physeq_presab_pruned_tree## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 2911 taxa and 24 samples ]
## sample_data() Sample Data: [ 24 samples by 44 sample variables ]
## tax_table() Taxonomy Table: [ 2911 taxa by 8 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 2911 tips and 2909 internal nodes ]# First force the OTU
randomized_otu <- matrix(otu_table(random_physeq_presab_pruned_tree), nrow = nrow(otu_table(random_physeq_presab_pruned_tree)))
rownames(randomized_otu) <- sample_names(random_physeq_presab_pruned_tree)
colnames(randomized_otu) <- taxa_names(random_physeq_presab_pruned_tree)
## Calculate input for SES_MPD
# Convert the presence/absence data to standardized abundanced vegan function `decostand' , NOTE: method = "pa"
otu_decostand <- decostand(randomized_otu, method = "pa")
# check total abundance in each sample
apply(otu_decostand, 1, sum)## MBREJ515 MBREJ715 MBREJ915 MBREK515 MBREK715 MBREK915 MDPEJ515 MDPEJ715 MDPEJ915 MDPEK515 MDPEK715 MDPEK915 MINEJ515 MINEJ715 MINEJ915 MINEK515 MINEK715 MINEK915 MOTEJ515 MOTEJ715 MOTEJ915 MOTEK515 MOTEK715 MOTEK915
## 906 574 434 268 256 358 493 447 476 276 284 381 840 632 586 452 383 511 505 343 444 274 238 381# check for mismatches/missing species between community data and phylo tree
randomized_matches <- match.phylo.comm(randomized_tree, otu_decostand)
# the resulting object is a list with $phy and $comm elements. replace our
# original data with the sorted/matched data
phy_randomized_rm2 <- randomized_matches$phy
comm_randomized_rm2 <- randomized_matches$comm
# Calculate the phylogenetic distances
phy_dist_randomized_rm2 <- cophenetic(phy_randomized_rm2)
## Calculate SES_MPD
###################################### INDEPENDENT SWAP ############################################
# calculate standardized effect size mean pairwise distance (ses.mpd)
unweighted_sesMPD_indepswap_randomized <- ses.mpd(comm_randomized_rm2, phy_dist_randomized_rm2, null.model = "independentswap",
abundance.weighted = FALSE, runs = 999)
df <- unweighted_sesMPD_indepswap_randomized
df$names <- row.names(df)
df_2 <- make_metadata_norep(df) %>%
mutate(fraction = fct_recode(fraction, Particle = "WholePart", Free = "WholeFree"))
# Is there a relationship between richness and Unweighted Mean Pairwise distance?
summary(lm(ntaxa ~ mpd.obs.z, data = df_2))##
## Call:
## lm(formula = ntaxa ~ mpd.obs.z, data = df_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -214.60 -128.20 -13.57 56.63 465.39
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 448.65 35.29 12.713 1.3e-11 ***
## mpd.obs.z 75.18 95.93 0.784 0.442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 172.8 on 22 degrees of freedom
## Multiple R-squared: 0.02716, Adjusted R-squared: -0.01706
## F-statistic: 0.6142 on 1 and 22 DF, p-value: 0.4416summary(lm(ntaxa ~ mpd.obs.z, data = filter(df_2, fraction == "Particle")))##
## Call:
## lm(formula = ntaxa ~ mpd.obs.z, data = filter(df_2, fraction ==
## "Particle"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -208.88 -109.98 -51.42 39.11 346.59
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 557.41 50.97 10.936 6.96e-07 ***
## mpd.obs.z -18.72 153.75 -0.122 0.906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 175.3 on 10 degrees of freedom
## Multiple R-squared: 0.00148, Adjusted R-squared: -0.09837
## F-statistic: 0.01482 on 1 and 10 DF, p-value: 0.9055summary(lm(ntaxa ~ mpd.obs.z, data = filter(df_2, fraction == "Free")))##
## Call:
## lm(formula = ntaxa ~ mpd.obs.z, data = filter(df_2, fraction ==
## "Free"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -108.71 -51.55 -17.28 57.16 160.49
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 343.12 25.00 13.727 8.17e-08 ***
## mpd.obs.z 68.12 62.35 1.093 0.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 85.34 on 10 degrees of freedom
## Multiple R-squared: 0.1067, Adjusted R-squared: 0.01732
## F-statistic: 1.194 on 1 and 10 DF, p-value: 0.3002randomized_rich_plot <- ggplot(df_2, aes(y = ntaxa, x = mpd.obs.z, fill = fraction)) +
geom_vline(xintercept = 0, linetype = "dashed", size = 1.5) +
geom_point(size = 3.5, aes(shape = season)) + ylab("Randomized Richness") +
xlab("Unweighted Phylogenetic Diversity") +
scale_fill_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
scale_x_continuous(limits = c(-1,1)) +
theme(legend.title = element_blank(), legend.position = "none")
plot_grid(randomized_rich_plot, season_legend,
rel_heights = c(1, 0.05), nrow = 2, ncol = 1)
Plot: Temp vs Production
ggplot(lasso_data_df, aes(x = Temp_C, y = frac_bacprod, color = fraction)) +
geom_point(size = 3.5, aes(shape = season, fill = fraction), color = "black", guide = FALSE) +
xlab("Water Temperature (C)") + ylab("Bulk Community Production (ug C/L/day)") +
scale_fill_manual(values = fraction_colors, guide = 'none') +
scale_color_manual(values = fraction_colors) +
scale_shape_manual(values = season_shapes) +
geom_smooth(method = "lm", fill = NA, lty = 2, aes(color = fraction)) +
theme(legend.position = c(0.5, 0.95), legend.direction = "horizontal",
legend.title = element_blank())
free_data_df <- dplyr::filter(lasso_data_df, fraction == "Free")
summary(lm(frac_bacprod ~ Temp_C, data = free_data_df))##
## Call:
## lm(formula = frac_bacprod ~ Temp_C, data = free_data_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.448 -11.678 -3.663 6.226 35.044
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.3131 27.3683 1.510 0.162
## Temp_C -0.9054 1.4099 -0.642 0.535
##
## Residual standard error: 18.02 on 10 degrees of freedom
## Multiple R-squared: 0.03961, Adjusted R-squared: -0.05643
## F-statistic: 0.4124 on 1 and 10 DF, p-value: 0.5352particle_data_df <- dplyr::filter(lasso_data_df, fraction == "Particle")
summary(lm(frac_bacprod ~ Temp_C, data = particle_data_df))##
## Call:
## lm(formula = frac_bacprod ~ Temp_C, data = particle_data_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.775 -4.172 -1.356 3.410 15.237
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.6913 11.3062 2.715 0.0218 *
## Temp_C -1.0879 0.5825 -1.868 0.0913 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.444 on 10 degrees of freedom
## Multiple R-squared: 0.2586, Adjusted R-squared: 0.1845
## F-statistic: 3.489 on 1 and 10 DF, p-value: 0.09133Figure S1: Faiths PD
surface_PAFL_otu_pruned_RAREFIED_rm2## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1891 taxa and 24 samples ]
## sample_data() Sample Data: [ 24 samples by 70 sample variables ]
## tax_table() Taxonomy Table: [ 1891 taxa by 8 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1891 tips and 1889 internal nodes ]## Calculate Faith's PD and species richness for sample
faiths_pd_RAREFIED <- pd(comm_RAREFIED_rm2, phy_RAREFIED_rm2, include.root = FALSE)
faiths_pd_RAREFIED$norep_filter_name <- row.names(faiths_pd_RAREFIED)
# Join Faith's PD with the rest of the metadata
meta_data_PD <- left_join(faiths_pd_RAREFIED, lasso_data_df, by = "norep_filter_name")
### Is there a correlation between species richness and faith's PD?
lm_PD_vs_SR <- lm(PD ~ SR, data = meta_data_PD)
ggplot(meta_data_PD, aes(y = PD, x = SR)) +
geom_point(size = 3) +
ylab("Faith's PD") +
xlab("Species Richness") +
geom_smooth(method = "lm") +
annotate("text", x = 600, y=35, color = "black", fontface = "bold",
label = paste("R2 =", round(summary(lm_PD_vs_SR)$adj.r.squared, digits = 3), "\n",
"p =", round(unname(summary(lm_PD_vs_SR)$coefficients[,4][2]), digits = 20))) 
summary(lm_PD_vs_SR)##
## Call:
## lm(formula = PD ~ SR, data = meta_data_PD)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.93991 -0.96092 0.03063 1.00013 2.60206
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.836131 0.986599 10.98 2.13e-10 ***
## SR 0.062600 0.002285 27.40 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.52 on 22 degrees of freedom
## Multiple R-squared: 0.9715, Adjusted R-squared: 0.9702
## F-statistic: 750.9 on 1 and 22 DF, p-value: < 2.2e-16